同步磁阻电机的在线参数辨识及无位置传感器控制

The purpose of this study was to construct a sensorless control system with parameter identification for a synchronous reluctance motor (SynRM). In this paper, initial position estimation and parameter identification at standstill are successfully demonstrated. A high-frequency voltage injection method was applied for initial position estimation and a recursive least squares (RLS) method was used for identification of the resistance and inductance, as well as the voltage drop caused by the inverter. The proposed system was examined experimentally and good results were obtained.

No abstract available

The effectiveness of sensorless control of synchronous reluctance motors (SynRM) is affected by the accuracy of the inductance. Due to the presence of severe magnetic saturation in SynRM, inaccurate inductance parameters can produce observation angle errors, which affect the performance of the sensorless control system of the motor. In order to improve the robustness of the sensorless control system against parameter variations, this paper proposes a four-vector high-frequency injection (FV-HFI) sensorless method for online inductance identification of SynRM. Based on the accurate magnetic saturation model of the d-axis inductance, the method achieves accurate angle estimation by online estimation of the q-axis self-inductance and dq-axis mutual inductance. The simulation model is constructed based on the test results of a 3kW SynRM motor. The validity of the proposed method in suppressing the angle estimation error is verified by this simulation platform.

Many modern control strategies of synchronous reluctance machines (SynRM) require precise output torque, however, torque measurement systems usually introduce additional devices into the system, occupy additional volume and expend additional cost. As one of the solutions, torque can be estimated, but the methods are always sensitive to machine parameters variation. This paper purposes a new and useful method for SynRMs parameter identification and torque estimation based on filter-free high frequency (HF) signal injection, which can provide accurate torque value with low system complexity and low chips computing load, and verifies the feasibility of the scheme by giving experiment result.

To improve the efficiency and stability of synchronous reluctance motor (SynRM) under sensorless control strategy, a novel parameter identification method based on an adaptive alternate extended Kalman filter (AAEKF) is proposed in this paper. The core idea is to design two EKFs based on different state variables, which executed alternately. The first EKF is employed to estimate rotor speed and d-axis inductance simultaneously, the other is used to estimate rotor speed and q- axis inductance. Meanwhile, the estimated inductances are used to achieve online maximum torque per ampere (MTPA) control. Furthermore, to improve the estimation accuracy under different working conditions, an adaptive system noise covariance matrix based on innovation sequence is incorporated into EKFs, which reduce the number of parameters to be adjusted at the same time. The proposed control method is validated by simulation and experiment.

No abstract available

The saliency investigation of synchronous reluctance motor (SynRM) is essential for high-performance control, such as high-frequency-injection (HFI) based sensorless control and maximum torque per ampere (MTPA) control. However, the conventional methods focus on the primary saliency but neglect the secondary saliency that causes the parameter variation of the motor with electrical angles, which leads to degradation of the control performance. This paper proposes an experimental method for modeling the saliency of SynRM, which considers both primary and secondary saliency. The proposed method estimates the incremental inductance and then calculates the flux and apparent inductance based on the estimated incremental inductance. To address the rank-deficient issue of the conventional HFI method in estimating the incremental inductance, a four-vector HFI method is proposed to make the identification full-rank. Compared with the conventional modeling methods, the proposed method can model not only the variation of the inductance and flux parameters with currents but also the parameter variation with electrical angles. The effectiveness of the proposed method is validated on a 3-kW SynRM drive.

No abstract available

The position sensorless control system of synchronous reluctance motor (SynRM) has the advantages of low cost and high reliability. A position estimation error analysis method for SynRM under the condition of multi-parameter mismatch in the back-EMF-based position estimation method is proposed in this paper, which considers the effect of simultaneous mismatch of Rs and Lq on the estimation of back-EMF. The mathematical relationship between the position estimation error and the multi-parameter error is obtained by establishing the phase diagrams of the back-EMF and the current. The influence law of the multiparameter mismatch on the position estimation error in the maximum torque per Ampere control and minimum excitation current control modes is analyzed by simulation and experiment. The proposed method can analyze the variation mechanism of position estimation error with a multi-parameter mismatch, current angle, and rotational speed, which provides a theoretical basis for the design of a highly robust position estimation method.

Due to the non-linear change of the inductance, the control strategy with constant parameters does not match with the actual operating condition of the motor, which seriously affects the dynamic performance of the motor. Therefore, a flux weakening control strategy of permanent magnet assisted synchronous reluctance motor based on parameter identification is proposed in this paper. Firstly, based on the $\boldsymbol{d} \boldsymbol{-} \boldsymbol{q}$ axis equivalent circuit of the permanent magnet assisted synchronous reluctance motor, the root cause of the flux weakening control to improve the speed regulation ability of the motor is proved. Secondly, in order to obtain the actual inductance parameters during the operation of the motor, a parameter identification method based on the principle of high frequency injection is proposed. The real-time inductance parameters are calculated by the response signal obtained by high frequency injection, and then the real-time inductance parameters are fed back into the flux weakening control strategy of the motor to improve the stability of the motor operation. Finally, the effectiveness of the proposed method is verified by simulation.

Parameter mismatch is the main factor that causes severe position estimation error for sensorless permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) drives. In this article, a robust hybrid flux observer (HFO) based on extended state observer (ESO) with active flux error estimation is proposed to improve the robustness of parameter mismatch. Based on the analysis of parameter mismatch sensitivity of traditional HFO, an ESO is constructed to obtain the active flux estimation error caused by parameter mismatch in the current model. The active flux estimation error can be compensated by feedback loop to improve the accuracy of rotor position estimation. Meanwhile, both the influence of ESO parameters and the stability are analyzed. The proposed method is independent of the precise dq-axis inductance parameters and flux linkage of the motor, which has strong robustness to parameter mismatch. Finally, experiments are carried out on a platform of 2.2-kW PMa-SynRM drive to verify the effectiveness of the proposed method.

This article studies the parameter robustness issues encountered in the application of nonlinear flux observer (NFO) in position-sensorless control of synchronous reluctance motors (SynRMs). The nonlinear structure of the NFO poses difficulties for parameter robustness analysis, which has only been studied through simulation in the past. In addition, the magnetic saturation characteristics of SynRMs exacerbate the risk of parameter mismatch. These factors pose challenges to the stability of NFO-based SynRMs sensorless control. In response to this problem, this article elaborates on a methodology dedicated to parameter robustness analysis and enhancement. First, for the NFO based on inductance lookup tables (LUTs), the coordinate transformation is applied for its error system analysis to evaluate the impact of parameter mismatch. Then, the convergence analysis of the error system with parameter mismatch is realized using the phase portrait. Considering the distributed effects of parameter mismatch on the NFO, the idea of lumped position error compensation is investigated to enhance the parameter robustness of the NFO. The position error caused by parameter mismatch can be extracted for compensation by estimating an auxiliary flux vector under the estimated dq-axes. Thus, a position error estimator based on flux observer is developed to compensate for the position error caused by the parameter mismatch. Comparative experiments are conducted in a 7.5-kW SynRM platform to verify the studied algorithm.

This article proposes a sensorless maximum-torque-per-ampere (MTPA) integrated control strategy to maximize the driving efficiency of synchronous reluctance motors (SynRM) having cross-saturation effects without any position sensors. Differing from the conventional method that the rotor position estimation and the MTPA tracking trajectory are two separate processes, the proposed strategy can achieve sensorless MTPA one-step direct control of the SynRM. With the proposed strategy, five independent equations are firstly constructed and extracted from the pulsewidth modulation (PWM) induced transient excitation currents. By solving these equations, the optimal MTPA current space vector angle can be calculated directly without any motor parameters, which not only greatly simplifies the whole implementation process, but also effectively eliminates the influences of the motor saturation and parameters uncertainties. Meanwhile, aiming at those inapplicable modulation areas where there are not enough suitable voltage vectors to satisfy minimum time demand for the slope sampling of response currents, such as ultralow or ultrahigh modulation, and sector-boundary region, the corresponding dimensionality reduction compensation scheme and modified PWM compensation solution are described in detail. Finally, experimental results indicate that the proposed direct control strategy can achieve the high-performance sensorless control with accurate MTPA self-tracking operation in the entire linear modulation region.

This paper proposes an active-flux-based rotor position and speed estimation scheme for sensorless-vector- controlled synchronous reluctance motor (SynRM) drives, which is comprised of two main parts: 1) a super-twisting sliding mode observer (ST-SMO) for estimating the derivatives of active flux components; and 2) a rotor position and speed estimator based on the phase-locked loop (PLL). The ST-SMO is designed based on the active flux model of the SynRM in the stator reference frame. The estimated active flux components, which are obtained based on the ST-SMO and the integrator, are received as inputs of the PLL, and the estimated rotor position and speed are provided as outputs of the PLL. The feasibility and effectiveness of the proposed estimation scheme have been verified by the simulation results.

This paper compares different control strategies of the synchronous reluctance motor to outline their impact on the accuracy of flux-observer based sensorless operation. Maximum torque per ampere, maximum efficiency, and maximum power factor controls, usually referred to optimize the operating performance of synchronous reluctance motors are considered. Alternative solutions not usual in literature but specifically investigated for supporting the estimation are also considered, namely constant direct-axis-current and constant direct-axis-flux controls. The flux-observer detects the flux components in the two-phase stationary reference frame by a non-linear model achieved by finite-elements computations. An auxiliary mechanical observer who accounts for the finite-elements mapping of the torque is adjusted by the flux estimation error and provides the rotor position and speed needed for sensorless control. An extended set of experimental tests is presented where the different control methods are compared in terms of position and speed estimation errors and overall control quality. A four poles, three kW synchronous reluctance prototype designed for general industry application is used for testing.

The sensorless control of synchronous reluctance motor (SynRM) in the zero and low‐speed domain using the traditional high‐frequency injection method has the problems of audible noise caused by the high‐frequency injection and high‐frequency loss caused by the change of working conditions, which limits the practical application of SynRM in the industrial field. To solve the problem of high‐frequency noise and loss, a pseudo‐random high‐frequency injection method considering parameter variation is proposed in this paper. Firstly, the signal injection form was adjusted to expand the power spectral density of the high‐frequency current, and the current energy spike was suppressed to reduce high‐frequency noise. Secondly, the current demodulation was combined with the flux map model to complete the injection voltage amplitude adjustment, so that the response current was kept constant under multiple operating conditions to reduce the high‐frequency loss. At the same time, the flux map model is applied to the observer to reduce the rotor position estimation error caused by the cross‐coupling effect. Under the condition of satisfying the dynamic and steady performance requirements of sensorless control, the high‐frequency loss and sharp noise caused by the high‐frequency injection method are effectively suppressed. Finally, experiments were carried out on a 1.5 kW SynRM drive platform to verify the feasibility and effectiveness of the sensorless control scheme in this paper.

Speed sensorless control is an efficient technique for providing reliable, cost-effective, and stable drive system in electric vehicle (EV). Yet, obtaining accurate sensor-less control across entire speed range poses a significant problem. This paper presents a modified disturbance observer (MDO) based speed sensor-less control of synchronous reluctance (Syn-Rel) motor. The presented technique utilizes active flux (AF) concept of Syn-Rel motor, which eliminates variations in the estimated flux from cross-coupling. Presented system’s robustness is enhanced by its insensitivity to DC bias and high-frequency noise. Speed and position are estimated with adaptive gain enhanced quadrature PLL (AG-eQPLL) technique. In comparison to conventional and other speed estimation techniques, presented technique is with minimal speed and position error. Presented system is integrated with solar PV array and grid for augmenting battery performance. The battery performance and range are improved utilizing roof mounted solar panel under running and at rest condition of vehicle. Moreover, performance is also improved with battery charging using installed voltage source converter (VSC) acting as an on-board charger under standstill condition. The system is analysed and validated using MATLAB software. A comprehensive depiction of EV dynamics with improved performance is also validated through experimental test bench. Note to Practitioners—This work is motivated by need for a reliable and efficient sensorless control strategy for electric vehicle (EV) drives employing synchronous reluctance (Syn-Rel) motors. Traditional encoder-based drives increase system cost and reduce reliability under harsh automotive conditions. Proposed modified disturbance observer (MDO) based control scheme, integrated with an adaptive gain-enhanced quadrature PLL (AG-eQPLL), enables accurate estimation of rotor speed and position without mechanical sensors, even under parameter variations and noise. The incorporation of active flux (AF) modeling enhances robustness and simplifies implementation. Moreover, the integration of solar PV and grid-based charging improves energy utilization and extends battery life. Practitioners can adopt this approach to design cost-effective, compact, and energy-optimized EV drive systems that maintain stable performance across wide speed ranges. Presented control algorithm can be implemented in real-time using standard digital controllers, making it practical for commercial EV and hybrid drive applications.

Synchronous reluctance motors (SynRMs) have attracted attention due to their low cost and high efficiency. Flux-weakening (FW) control can be used to control SynRMs with high efficiency at high speed. However, it is difficult to derive FW conditions for SynRMs because their inductance varies significantly with current due to magnetic saturation. Voltage feedback FW control achieves high-speed operation by maintaining a constant armature voltage without using motor parameters. This paper proposes a voltage feedback FW control method for SynRMs that improves current control characteristics under FW conditions by adding a disturbance observer in the current control loop. The experimental results show the effectiveness of the proposed method.

The flux linkage characters of synchronous reluctance motors (SynRM) are usually tested offline due to a significant cross-saturation effect. In this paper, a self-commissioning method with current injection is proposed, in which the cross-saturation effect is considered. The reference equation for the injection current is derived in order to avoid potential rotor motion during the injection, and it ensures that sufficient valid data is distributed in the current plane after the injection. Considering the difficulty of PI tuning under unknown motor pa-rameters, a current controller based on general-integral extended state observer (GI-ESO) is applied to track the given current. Experiments are performed on a 3kW SynRM platform to verify the validity of the proposed method. The proposed method offers a promising solution for industrial applications of SynRM.

The paper presents a framework for the design and analysis of position observers for sensorless control of synchronous reluctance machines. An improved inductance model is developed to account for the position error induced inductance variations. The instability regions of active flux based position observer are analytically identified and validated. A novel technique, Adaptive Projection vector for Position error estimation (APP), that alleviates the stability problems is introduced. Furthermore, the proposed technique can be augmented with a second projection vector to estimate speed error independently of the position error, referred to as Adaptive Projection vector matrix for Position and Speed error estimation (APPS). Stability and performance of proposed technique is validated on a 1 kW synchronous reluctance motor test bench.

This article proposes a sensorless control method for a dual three-phase synchronous reluctance motor utilizing an improved hybrid flux observer to compensate position estimation errors caused by magnetic saturation and cross-coupling effects. A hybrid flux observer based on the current model and voltage model is used to extend the operational speed range of the system. The impact of inductance nonlinearity and mutual inductance caused by magnetic saturation and cross-coupling effects on the position observation accuracy is thoroughly analyzed. Based on this analysis, a compensation method is introduced to improve the position observation accuracy under these conditions. In addition, a disturbance observer based on the extended state observer is introduced to improve the system's robustness to external and internal disturbances. An experimental platform was built to validate the proposed method, with experimental results demonstrating its effectiveness and feasibility.

No abstract available

Synchronous reluctance motor (SynRM) is a special synchronous motor that completely utilizes reluctance torque to operate. Because of its low cost, solid structure and no risk of demagnetization at high temperatures, it has gradually attracted attention in recent years. In order to increase the reliability of the control system and take advantage of the low cost of synchronous reluctance motor, a sensorless control strategy in wide speed range is introduced in this paper. In low speed range, a high frequency voltage injection method is used to get the rotor position information. In middle or higher speed range, an active flux observer is introduced to realize the vector control. For the purpose of maintaining the stability, we adopted a weighted function method during the switching between these two observation methods. Finally, the proposed method is verified by experiments on a 5.5-kw SynRM which can work well down to 0 rpm and up to 1500rpm.

Affected by the magnetic saturation effect and unmodeled dynamics, the parameters of the synchronous reluctance motor (SynRM) are highly nonlinear and time-varying. The resulting unreasonable current loop reference command severely restricts the maximum efficiency and high control performance of SynRM. Therefore, an adaptive non-singular terminal sliding mode control scheme for SynRM drive system is proposed to improve the dynamic performance and robustness. Firstly, an analytical model of flux linkage and inductance that satisfies the energy conversion mechanism is proposed to estimate the required parameters of the control system in real time. Secondly, a novel fast finite time adaptive-gain reaching law is proposed to shorten the arrival time while reducing the chatter near the sliding mode surface. Then, a non-linear disturbance observer is designed to estimate the total disturbance of the system. The asymptotic stability of the system is proved by Lyapunov’s theorem. The experimental results demonstrate that the system has satisfactory dynamic performance and robustness.

This paper presents the closed-loop flux observer (CLFO) for position and speed estimation and control of synchronous reluctance (Syn-RL) motor drive for electric propulsion-based vehicles. In comparison to conventional open loop observer, the presented closed loop observer reduces the non-linearity such as DC drift, saturation, delay and attenuation present in the speed estimation for the EV system. The prime source of energy is the battery, which is integrated with bidirectional DC-DC converter. In this paper interleaved parallel bi-directional DC-DC (IPBDC) converter is presented. The improved performance of the presented system is also highlighted in this paper with an emphasis on battery performance with solar PV integration. Performance of the CLFO-based Syn-RL motor drive is observed and authenticated in MATLAB/Simulink.

This paper presents a sensorless control of synchronous reluctance motor (SynRM) over full speed range. The control algorithm is based on Model Reference Adaptive System（MRAS） at low speed and flux observer at medium and high-speed. Moreover, considering the serious core saturation of SynRM, which includes cross saturation between the d-axis and q-axis, an off-line parameter adaptive algorithm is used in the sensorless control algorithm over full speed range. The simulation results show that the parameter adaptive algorithm dramatically improves the position estimation accuracy.

The speed sensorless control of synchronous reluctance (SynRL) motor for electric vehicle with improved flux estimation based on third order generalised integrator flux observer(TOGIFO) is presented in this paper. Sensor reduction is crucial in electric vehicle (EV) drives based on synchronous reluctance (SynRL) motor in order to increase system dependability and robustness. Moreover, achieving seamless wide-range speed control without sensors is still difficult. When compared to the conventional stator flux linkage estimation method for accurate speed estimation, the sluggishness of the drive at low and high speeds of operation is mitigated by the improved speed and position estimation. A bi-directional DCDC Converter (B-DC) is used with the battery for improved performance of the system.The paper also discusses the improved battery performance for enhancing vehicle's range. The effectiveness of the presented system is verified through comprehensive MATLAB/Simulink validation.

Speed sensor less control is an efficient technique for providing reliable, cost-effective, and stable drive system in electric vehicle (EV). Yet, obtaining accurate sensor-less control across the entire speed range poses a significant problem. This paper presents a modified disturbance observer (MDO) based speed sensor-less control of synchronous reluctance (Syn-Rel) motor. The presented technique utilizes the active flux (AF) concept of the Syn-Rel motor, which eliminates the variations in the estimated flux from the cross-coupling. The presented system's robustness is enhanced by its insensitivity to DC bias and high-frequency noise. The speed and position are estimated with enhanced quadrature PLL (e-QPLL) technique. In comparison to conventional and other speed estimation techniques, the presented technique is with minimal speed and position error. The presented system is integrated with solar PV array and grid for augmenting battery performance. The battery performance and range are improved utilizing roof mounted solar panel under running and at rest condition of the vehicle. Moreover, performance is also improved with battery charging using installed voltage source converter (VSC) acting as an on-board charger under standstill condition. The system is analysed and validated using MATLAB software. A comprehensive depiction of EV dynamics with improved motor battery performance is included.

. An enhanced direct torque control (E-DTC) system of a synchronous reluctance motor (Syn-RM) is presented in this paper. The motor system is modelled by taking into account its non-linear behaviours such as iron losses and magnetic saturation. The proposed method consists of incorporating hysteresis DTC with a model reference adaptive system (MRAS) flux observer. This technique is applied in order to achieve good torque and flux ripples reduction, which ensure a smooth operation of the Syn-RM along all the speed range. Furthermore, the proposed method has simple design and implementation in the overall control system, and can avoid the drawbacks of conventional flux estimators. Simulation results show the effectiveness of the proposed method.

Reducing the number of sensors is a key part of electric vehicle (EV) drives that use synchronous reluctance (Sync-ReI) motor. This helps to make the system more reliable and stable. Nevertheless, getting sensorless control to work smoothly across a wide speed range is still a significant task. This paper presents the improved second-order SOGI-FLL-based flux observer for speed sensorless control of Sync-ReI motor. In contrary to the other approach for estimating stator flux linkage, this one provides more accurate speed estimates. The drive's sluggishness is reduced at both low and high speeds attributed to better speed and position estimation. This paper also addresses the augmented performance of the battery for the driving range. The presented system's performance is thoroughly examined and validated in MATLAB/Simulink.

No abstract available

No abstract available

Because of the special structure of synchronous reluctance motor (SynRM), the cross-coupling effect is severe. Aiming at reducing the position estimation error for low speed sensorless SynRM drives caused by the cross-coupling effect, a flux compensation method considering the stator mutual inductance is implemented on a reduced-order flux observer. After the stability and parameter analysis, the mutual inductance compensation method is proved to effectively reduce the flux estimation error, and consequently reduce the position estimation error of the flux observer. The proposed method was verified on a 3-kW SynRM drive platform, whose position estimation error is much less than the traditional observer.

No abstract available

This article proposes a fault-tolerant sensorless control strategy based on a frequency adaptive extended state observer (ESO) with complex coefficient filter (FAESO-CCF) for a $3\times 3$ -phase permanent magnet (PM)-assisted synchronous reluctance motor (PMA-SynRM) with single-phase open-circuit fault (OCF). By controlling the cooperative operation of each module, the torque ripple can be significantly reduced, thereby realizing the fault-tolerant operation under single-phase OCF. In order to meet the requirements of fault tolerance, the input voltage and current of the position sensorless are severely distorted, resulting in a decrease in the estimation accuracy of the rotor position. To solve this problem, the frequency adaptive ESO (FAESO) with bandpass filter properties is proposed. Moreover, a complex coefficient filter (CCF) is introduced to extract the third and fifth harmonics in the active flux estimated by FAESO, so that the bandwidth of FAESO can be appropriately relaxed and the response speed of the system can be improved. Meanwhile, a type-3 phase-locked loop (PLL) is designed to overcome the defect of type-2 PLL that there is a steady-state error during ramp acceleration, which further improves the accuracy of system. Finally, the accuracy and effectiveness of the proposed observer are evaluated by experiments.

No abstract available

This paper presents the Sensorless control of variable reluctance synchronous motor VRSM based on direct speed control (DSC) based on PLL technique. This technique reduces the absorbed current value and it ensures a high starting torque. The proposed control has many advantages such as: the reduction the hardware implementation complexity, the low cost, offers a reduced size, elimination of direct sensor wiring, better noise immunity, increased reliability and less maintenance requirements. Thus, based on these stated benefits and to estimate the speed as well as the rotor flux, we have proposed a model reference adaptive system observer (MRAS). Simulation results show the effectiveness and feasibility of the proposed Sensorless vector control.

This article explores the implementation and comparative analysis of Sliding mode observer (SMO) based Model Predictive Torque Control (MPTC) for Permanent Magnet Assisted Synchronous Reluctance Motor (PMa-SynRM) versus conventional Model Predictive Torque Control. The proposed SMO-based MPTC strategy aims to enhance the performance and efficiency of PMa-SynRM by eliminating the need for mechanical sensors, thus reducing system complexity and cost. The conventional MPTC method, while effective, relies on precise sensor data for optimal performance, which can be a limitation in specific applications. This paper presents an SMO-based sensorless control algorithm that leverages state estimation techniques to accurately predict and control the torque and flux linkage of the PMa-SynRM. Through extensive simulations, the effectiveness of the sensor-less MPTC in maintaining robust performance under various operating conditions is demonstrated. The results indicate that the sensor-less approach not only matches but sometimes surpasses the conventional MPTC in terms of dynamic response, efficiency, and robustness. This research underscores the potential of sensor-less control methodologies in advancing the performance and applicability of modern electric machines in Electric Vehicles.

No abstract available

The synchronous reluctance motor (SynRM) has attracted attention as a low-cost, resource-saving, and high-efficiency motor. Maximum torque per ampere (MTPA) control is one method to control the SynRM with high efficiency. However, it is difficult to derive MTPA conditions for the SynRM because its inductance varies significantly depending on the current due to magnetic saturation. This paper proposes an online MTPA angle search method. In the proposed method, the flux linkage plane is modeled, and the model parameters are identified using the recursive least squares (RLS) method. The MTPA search is achieved using the estimated flux linkage plane and the gradient ascent method. This study conducted various experiments to evaluate the performance of the proposed online MTPA angle search method. The proposed method is effective for a wide current range, resistance variation, and position error.

Aiming at the rank-deficient issue of the full-parameter identification model of synchronous reluctance motor (SynRM), an online full-parameter identification scheme combined with the reclusive least square (RLS) algorithm and the least mean square (LMS) algorithm is proposed in this paper. Due to the characteristics that the inductance parameters vary rapidly with the currents, while the stator resistance varies slowly with the winding temperature, the RLS algorithm with a faster convergence rate is proposed to identify the inductance parameters quickly, and based on the RLS identification results, the LMS algorithm with less calculation is used to update the stator resistance. This method avoids the effect of using fixed parameters to identify the other parameters, and it can follow the changes in motor parameters well. Finally, the simulation results in the MATLAB/Simulink verify the effectiveness of the proposed scheme.

Synchronous Reluctance Machine (SynRM) has recently gained attention for electric vehicle application due to its lightweight construction. However, one of the major drawbacks of such motors is that the power factor is low. Therefore, it is important to operate such motors in the maximum power factor condition. Such an operation calls for correct values of inductance. In this paper, a method to estimate stator inductance has been proposed using the Model Reference Adaptive System. Two new functional candidates have been derived that estimate the d-axis and q-axis stator inductances to account for its variation due to magnetic cross saturation. Online inductance adaptation can lead to a significant improvement in the SynRM Power Factor. The proposed theory has been extensively tested by simulation studies on MATLAB SIMULINK and its effectiveness has been verified.

The accuracy of position estimation for synchronous reluctance motor (SynRM) sensorless control suffers from the magnetic saturation effect and uncertain system noise, especially in the conditions of dynamic, low-speed operation, and parameter mismatch. In this article, a robust sensorless control strategy built on a positive definite Sage-Husa Kalman filter (PDSH-KF) considering the dynamic magnetic saturation effect is proposed. First, the static inductance and dynamic inductance caused by magnetic saturation are included in the establishment of the extended back EMF (EEMF) model. Furthermore, relying on the Kalman filter (KF), an EEMF estimator based on PDSH-KF is proposed to track the position of SynRM, in which the mean and covariance of the system noise can be adjusted online. Moreover, the observer divergence caused by the negative definite noise covariance matrix is avoided. In this way, the EEMF can be estimated accurately under varying operating conditions. Finally, the operating-frequency related EEMF component is extracted in no phase lag way by second-order generalized integration. The effectiveness of the proposed sensorless control scheme is verified through an experimental prototype.

A Permanent Magnet-Assisted Synchronous Reluctance Motor (PMASynRM) is favored because of its lower Permanent Magnet (PM) amount. They utilize more reluctance than PM torque compared to Interior Permanent Magnet Synchronous Motor (IPMSM) and Surface Permanent Magnet Synchronous Motor (SPMSM). Understanding the motor parameters at each operating point is crucial to achieving maximum efficiency. Variation of motor parameters due to temperature using offline models has been reported in the literature. These methods are computationally intensive, especially when the effect of cross-saturation is included in the models. Online parameter estimation is more impressive in real applications to develop a high-performance control technique as a result of these limitations, particularly when motors confront highly nonlinear structures such as PMASynRMs. An Artificial Neural Network (ANN) is presented in this paper for online estimation of inductances at the Rotor Reference Frame (RRF) to ensure optimum performance for model-based control systems. An online-tuned dynamic model is implemented by the proposed ANN and compared with Finite Element Analysis (FEA) data and experimental validation tests.

When the current dependent direct and quadrature inductances of a synchronous reluctance machine are calculated using measurement results, the estimated inductance values can drastically deviate from the actual ones for small current values. This paper presents an investigation of these deviations and introduces two methods to minimize the estimation errors, which consider the effects of the residual magnetism. Experimental results are also presented for a 12 kW reluctance machine.

Synchronous reluctance motors (SynRMs), which are characterized by the absence of permanent magnets and rotor windings, have garnered significant attention in recent years. A notable characteristic of SynRMs is the substantial variation in the inductances $L_{d}$ and $L_{q}$ along the d and q axes, which is influenced by magnetic saturation and the current flowing through each axis. Furthermore, cross-coupling interference occurs, where $L_{d}$ varies with the q-axis current, and $L_{q}$ varies with the d-axis current. Therefore, errors in motor parameters owing to variations in $L_{d}$ and $L_{q}$ influence the position sensorless control. To address these challenges, we propose a novel position sensorless control technique that leverages a SynRM model to estimate the high-frequency extended EMF generated by superposing high-frequency currents at low speeds. In this approach, when the circular superposition method, which superimposes high-frequency currents on both the d and q axes, is employed, the position estimation error converges even if a significant discrepancy exists between the actual and set values of $L_{q}$ of the SynRM model. By implementing this method and calibrating $L_{d}$ within the SynRM model to ensure convergence of the position estimation error, $L_{d}$ can be measured regardless of the variation in $L_{q}$. This paper presents experimental results that demonstrate the efficacy of this approach. In this study, we leverage this advantage to demonstrate that our method for measuring $L_{d}$, while considering cross-coupling interference yields highly valuable experimental results.

To improve the controlled current angle accuracy and reduce the copper loss for a Permanent Magnet assisted Synchronous Reluctance Machine (PMa-SynRM), this paper proposes an online Maximum Torque per Ampere (MTPA) control strategy based on Virtual High-Frequency Signal Injection (VHSI) to find its control current angle. The proposed method considers the nonlinear characteristics of the d- and q-axis inductance, d- and q-axis flux linkage, and permanent magnet flux. An error was identified in the mathematical determination of the MTPA control angle, stemming from the omission of the inductance's dependency on the current angle in the analysis. To solve the problem, an improved error supplementary control strategy considering permanent magnet flux mismatch was proposed, which features a lower calculation burden, less motor parameters information required, and higher precision. In this process, only permanent magnet flux information needs to be identified. The proposed MTPA detection and its supplementary control scheme was analyzed from mathematical derivation and verified by experiments.

Synchronous reluctance motors (SynRM) have a highly nonlinear flux-current mathematical relationship, making it difficult to obtain accurate inductance information. The Kalman-filter-based position estimation method is designed by using a single active back electromotive force (AEMF) model. When inductance parameter mismatch occurs, the estimation performance of the AEMF observer designed by using the single-model resonant Kalman filter (SM-RKF) is more sensitive to inductance parameter deviation as the load increases. To inhibit the effects of inductance parameter mismatch on the SynRM sensorless control system, the position estimation method based on a generalized multiple-model resonant Kalman filter (GMM-RKF) is proposed. First, a new AEMF model of the SynRM is derived at the maximum inductance-oriented coordinate frame. Second, the model set of GMM-RKF is built by RKF1 and RKF2 observers, which are derived from the two AEMF models. The two observers exhibit different parameter sensitivity characteristics at different current vector angle ranges. In comparison with SM-RKF, GMM-RKF has superior robustness against inductance mismatch since it combines the areas in two RKFs where the position estimation is robust to inductance mismatch. The effectiveness of the proposed sensorless control scheme is verified through an experimental prototype.

Synchronous reluctance motors (SynRMs) are notable for lacking magnets or and rotor windings. For control of torque of SynRMs, information about position of rotor is essential; however, sensorless control is preferred for improved reliability, cost reduction, and space saving. This approach is challenging at low velocities due to the reduced electromotive force from the flux. We proposed a position-estimation method using the SynRM model to estimate the high-frequency extended EMF (HF - EEMF), including position information, by superimposing small high-frequency (HF) currents using SynRM. The SynRM's inductance changed significantly based on the load. We discussed how the influence between the setting value and the actual value of the d-axis inductance in the SynRM model affected this position estimation method, and experiments verified its influence. Additionally, we introduced a method to measure the actual value of the d-axis inductance using experimental result. The proposed method was verified experimentally.

The evaluation of the SynRM saliencies for rotor position estimation using a high-frequency zero sequence signal (ZSS) injection strategy is presented in this paper. The strategy injects a ZSS and evaluates its response in the $\alpha-\beta$ component. This ZSS does not interact with the current control and the evaluation of its response is simple, nevertheless, it requires access to the neutral point of the machine. Experimental results show that the used auxiliary signal have the information of the SynRM saliencies and that it is possible to estimate rotor position with it. Also, new error compensation methods should be studied in order to improve the well-known rotor position estimation method based on the arctangent.

A position estimation method for the Synchronous Reluctance Motor (SynRM) based on an inductance model is proposed for light loads operating within the low to medium speed range. Under these conditions, the inductance exhibits a linear relationship with rotor position in specific regions. This linear characteristic of the inductance is utilized to determine the rotor position accurately. Simulations are conducted on a 1.0 kW FEM Model of SynRM prototype, validating the feasibility and effectiveness of the proposed method.

A rotor position observer is essential in the sensorless control of synchronous reluctance motors (SynRMs). However, conventional fixed-parameter position observers require repeated trials for parameter tuning and have poor dynamic performance. This paper proposes a novel Newton-Raphson based predictive phase-locked loop (NR-PPLL) to improve the dynamic performance, which does not require parameter tuning. A cost function is established based on the error between the stator flux estimated by the voltage equation and the one calculated with currents. For the search strategy of the conventional PPLL with multiple iterations and large computation, the NR method is adopted, which requires only three iterations to estimate the accurate position. Moreover, in response to the erroneous convergence of NR caused by inappropriate initial value selection, a simple correction approach is proposed. Finally, the effectiveness of the proposed NR-PPLL is verified on a 3-kW SynRM drive platform.

Pulsating high-frequency (HF) signal injection is a common saliency-based sensorless control method applied for synchronous reluctance motor (SynRM) drives. However, the accuracy of the rotor position estimation deteriorates due to severe cross-coupling effect and voltage fluctuations. To solve this problem, a measured reference frame HF voltage injection (MHFVI) method is proposed in this paper. A total of two discontinuous pulsating square-wave voltage signals are injected along with two measured axes, and position information is extracted with a cross-coupling factor (CCF) introduced, which can inherently account for the estimation error caused by the cross-coupling effect. The signal processing scheme of the proposed MHFVI method is further developed to be robust to voltage errors, meanwhile has less fluctuation in the position estimation error. The position estimation error is investigated and analyzed based on the finite element analysis results and the introduced CCF is calculated based on the SynRM current–flux characteristics models. Finally, the proposed method is verified by experiments on a 3-kW SynRM drive platform.

This paper introduces a Reluctance Equivalent (RE) EMF model for sensorless position estimation of synchronous reluctance motors (SynRMs). The unique salient structure of the SynRM results in varying reluctance paths associated with the stator phases. These variations in reluctance cause continuous changes in phase inductance relative to the rotor position, leading to the generation of RE EMF in the stator phase. The RE EMF model is initially analyzed in the abc plane and subsequently transformed into the stationary αβ plane for position estimation. The model’s validity is demonstrated using a Finite Element Model of the developed SynRM prototype

We show that the gradient observer proposed in Bernard & Praly, IFAC 2017 for PMSMs, can be used to estimate the stator flux of SynRMs, using only electrical measurements and the knowledge of the resistance. This sensorless observer ensures global convergence of the flux estimate provided the rotation speed and the current norm remains away from zero and the current and voltages are bounded, without requiring the knowledge of the magnetic model. Its robustness with respect to resistance errors is shown, with explicit expression of the resulting steady state error. This observer operates dynamically, in normal conditions, without any constraint on the load, and without any mechanical information. In a second step, we propose to exploit the knowledge of a magnetic inductance model (containing magnetic saturation) to estimate the rotor position from the flux estimate. The performance of this estimation in open-loop is illustrated on experimental data on a SynRM.

This article proposes a novel method to reduce rotor movement when estimating magnetic flux models for interior permanent magnet synchronous motors (IPMSMs) and synchronous reluctance motors (SynRMs). A rotation reduction technique that considers the flux saturation effect is proposed. A hysteresis controller is used to inject voltages only into the q-axis and simultaneously into the dq-axes. The rotation of the IPMSM is reduced by calculating the current at the point where the integral of the q-axis current over time becomes zero. In the case of SynRM, the rotation is reduced by calculating the injection voltage magnitudes such that the oscillation frequency of the q-axis current is higher than that of the d-axis current. The effectiveness of the proposed method was experimentally demonstrated using an 11-kW IPMSM and 1.5-kW SynRM. The proposed technique can be used for the self-commissioning of IPMSMs and SynRMs at a standstill, without rotor locking, or position sensors.

In this paper, a new sensorless approach is proposed to address the speed and position estimation of the Synchronous Reluctance Machine (SynRM). The design of the sensorless control algorithm is developed on the basis of the modified SynRM mathematical model employing a simple sliding mode observer (SMO) and a modified EMF observer that are connected in series. All variables of the modified SynRM model are expressed in the arbitrary rotating frame, which is the so-called estimated γδ reference frame. The derived modified rotor flux terms contain angle error information in the form of trigonometric functions. Initially, the modified rotor flux is expressed as a function of saliency and the stator current id, including the angular deviation between the dq and γδ reference frames, which are rotating at synchronous and estimated speeds, respectively. A suitably designed SMO is utilized to estimate the modified stator flux components in the γδ reference frame. Once the SMO operates in sliding mode, the derived equivalent control inputs of the flux/current observer are used to obtain the required angular position and speed information of rotor by means of the modified EMF and Speed/Position observer. Only measures of stator voltages and currents are required for the speed and position estimation. In addition, Lyapunov Candidate Functions (LCFs) have been applied to determine the sliding mode existence conditions and the gains of the modified EMF observer. The SynRM observer–controller system is tested and evaluated in a wide speed range, even at very low speeds, in the presence of torque load disturbances. Simulation results demonstrate the overall efficacy and robustness of the proposed sensorless approach. Moreover, simulation tests verify the fast convergence and high performance of the modified EMF/speed/angle observer.

This research introduces a high-performance sensorless control strategy for synchronous reluctance motor (SynRM) drives, utilizing a sliding mode observer (SMO) for the estimation of rotor position and velocity. The SMO is developed using the back electromotive force (Back-EMF) components of the SynRM within the stator reference frame. The stator current and voltage components in the stationary reference frame serve as inputs to the SMO, while the estimated rotor position and speed are obtained as outputs from the calculated Back-EMF components. Moreover, to enhance the capability of the entire drive system, a loss mitigation approach referred to as maximum torque per ampere (MTPA) is utilized to reduce rotor losses during steady-state operation. Finally, simulation outcomes obtained from MATLAB/SIMULINK are used to validate the operation of the sensorless control system for the SynRM utilizing the SMO.

This paper proposes a rotating square voltage injection (RSVI) based sensorless method for synchronous reluctance motor (SynRM). The proposed method injects a high-frequency (HF) rotating square voltage into the stator winding and extracts the fundamental and HF-induced currents without any filter. The elliptical shape of the HF-induced current trajectory is fitted using the least squares (LS) method. Based on the relationship between the ellipse tilt and the rotor position, the fitting coefficients of the elliptical current trajectory are used to estimate the rotor position by quadrature phase-locked loop (Q-PLL). The proposed position estimation method has a simple tuning procedure and does not require motor parameters. Furthermore, to improve the accuracy, the distortion of the elliptical current trajectory due to non-zero speed and position estimation error due to magnetic saturation are analyzed and compensated. Finally, the effectiveness of the proposed method is verified by carrying out simulations on a 3-kW SynRM.

In this paper, a novel SynRMs position sensorless control method based on high frequency voltage injection (HFVI) in measurement reference frame is presented to eliminate the rotor estimation error caused by cross-coupling magnetic saturation effect. To maximize rotor saliency signal information, the HF voltage signals are injected in measurement reference frame instead estimated reference frame which is mostly adopted by conventional HFVI based methods. And a cross-coupling coefficient is introduced to utilize both $d, q$ -axes current information to extract rotor position. The experiment results based on a 3kW SynRM drive system show the validity of the proposed sensorless control method.

No abstract available

Synchronous reluctance motors (SynRM) have been widely used in multiple fields. For SynRM, model predictive current control (MPCC) is one of the popular control methods nowadays. However, due to the current ripple caused by MPCC characteristics, sensorless control strategies cannot perform satisfactorily, which limits the application of MPC when sensorless control is required. The proposed method uses current prediction value to reduce the error of calculated high-frequency (HF) response, and estimates the rotor position and speed via compensated HF signal. This method can greatly improve the dynamic and static position estimation accuracy. Finally, the effectiveness of the improved strategy is proved by MATLAB/Simulink software.

No abstract available

: Synchronous reluctance motors (SynRM) are widely employed in industrial applications due to their high robustness, low cost, and absence of permanent magnets. In recent years, significant research efforts have focused on improving the controllability and efficiency of SynRM. Accurate rotor position information is essential for the controller to generate appropriate current and voltage references corresponding to the desired speed and load torque. Shaft-mounted position sensors are generally undesirable because of their high cost, sensitivity to harsh operating conditions, maintenance requirements, and reduced reliability in environments characterized by high vibration. Consequently, sensorless control techniques that estimate rotor position using measured stator currents and voltages have attracted increasing attention. However, magnetic saturation, parameter nonlinearities, and cross-coupling effects significantly degrade position estimation accuracy and may compromise the stability of sensorless SynRM drives. In this paper, a nonlinear SynRM model is developed using finite element analysis (FEA) to accurately capture magnetic saturation and cross-coupling effects, thereby providing a precise representation of the machine’s electromagnetic behavior under varying load and flux conditions. A series of magnetostatic FEA simulations is performed. To reduce computational complexity, only one motor pole is analyzed by applying anti-periodic boundary conditions along the domain sides and enforcing a zero magnetic vector potential on the external stator boundary. Nonlinear iron material properties are modeled using the appropriate B-H curve. The simulations are carried out by imposing d - and q -axis current components and computing the corresponding flux linkages and electromagnetic torque. Based on these results, both apparent and incremental inductances are extracted and incorporated into the control algorithm. An advanced fictitious flux linkage method combined with a phase-locked loop (PLL) is employed for accurate rotor position estimation. Simulation results confirm that the proposed sensorless control strategy ensures stable operation and high position estimation accuracy over the entire speed range.

A maximum torque per ampere (MTPA) sensorless control method for synchronous reluctance motor (SynRM) drive based on mixed high frequency signal injection is proposed in this paper. MTPA control strategy is achieved by virtual signal injection, which can avoid disadvantages of conventional MTPA methods such as low robustness to parameter variation and low dynamic performance. Additionally, the high frequency signal injection (HFSI) based method and extended back-EMF based method are respectively applied for the sensorless control of SynRM in the full speed range. The rotor position estimation errors caused by magnetic saturation and cross-coupling effect are specially considered. The validity of the proposed method is verified on a 3kW SynRM drive system.

The rotor position for electric motors, especially synchronous motor type, is an important information in speed / motion control applications. The position information of the rotating machines can be measured via incremental/absolute encoder, resolver or hall-sensors. However, these measuring devices have many disadvantages such as increased hardware complexity, high cost, increased volume, cable addition, decreased noise immunity, decreased reliability and increased maintenance. All these drawbacks can be overcome by using a sensorless algorithm. In this study, a Synchronous Reluctance Motor (SynRM) is controlled by using sensorless algorithm based on Artificial Neural Network (ANN). The algorithm presents not only position/speed estimation but also torque and flux observation which are critical to obtain maximum torque per ampere from SynRM. In this study, the motor is powered via solar photovoltaic panels. The environmental conditions such as irradiation and temperature affect the motor control performance. Therefore, the estimation performance of the ANN-based observation is analyzed under various environmental conditions. The results show that the sensorless system has satisfactory performance in all conditions.

Synchronous reluctance motors (SynRMs) have attracted attention for various applications because they have no magnets and rotor windings. Thus, various methods for rotor position sensorless control of SynRMs at low speeds have been proposed. Similarly, we previously introduced a method for rotor position sensorless control of SynRMs at very low speeds. The proposed control method superimposes a high-frequency current with a small constant amplitude using a high-frequency current control system. The $q$-axis inductance of the SynRM considerably changes according to the load. In this paper, we show that the rotor position estimation error converges even under a large error between the predefined and real settings of the $q$-axis inductance of the SynRM model for position estimation when using the proposed method. Experimental results confirm that the proposed method achieves robust rotor position sensorless control against $q$-axis inductance variations even at very low speeds.

To improve the position estimation for synchronous reluctance motor (SynRM) drives, an new full-order sliding mode observer (SMO) designed by using sinusoidal saturation function is presented. Firstly, the full-order SMO taking the extended electromotive force (EEMF) and the stator current as the state variables is built. Then, we would use a quadrature phase-locked loop (PLL) which can track the rotor position based on EEMF estimates. In view of reducing chattering phenomenon in traditional SMO, the sinusoidal saturation function is utilized to stand in for the switching function. Finally, according to experiments on a 3-kW SynRM drive, the proposed method is verified.

To solve the problem that the rotor estimation accuracy is greatly affected by parameter nonlinearity and high frequency oscillation, when the synchronous reluctance motor adopts the sensorless control system.This article proposes a new flux saturation model considering cross saturation effect applied to sensorless control of SynRM. The magnetic flux saturation data obtained by the hysteresis voltage injection method is used to estimate the model parameters, and the motor model under accurate magnetic flux saturation is obtained. The proposed magnetic flux saturation model is applied to the position sensorless control system of the synchronous reluctance motor. The proposed magnetic flux saturation model can effectively represent cross saturation and satisfy the reciprocity condition, and solve the nonlinear relationship between SynRM current and flux linkage. Finally, builting an experimental platform to verify the validity and correctness of the theoretical and experimental analysis.

This paper presents a novel method for mapping the $d$-axis and $q$-axis inductances ($L_{d}$ and $L_{q}$) of a Synchronous Reluctance Motor (SynRM), considering the effects of magnetic saturation (i.e., self-saturation and cross-saturation). The approach utilizes open-loop current-frequency ($i-f$) excitation for starting and running the motor. At the same time, encoder-based rotor position feedback is used only to transform measured quantities into the rotor reference frame for accurate inductance estimation. Unlike conventional rotor-locking or signal-injection techniques, the proposed method requires no additional hardware or complex control strategies, making it well-suited for laboratory characterization and parameter identification. Simulation and experimental results demonstrate that the method effectively captures inductance variation with respect to current, using only standard drive components. Its simplicity, accuracy, and practicality make it promising for embedded motor control and traction-oriented applications, including electric vehicle drives.

This paper illustrates the sensorless speed control of Synchronous reluctance motor (SynRM) using the Moving Horizon Estimation (MHE) based Kalman Filter (KF) method under sensor and process noise. The optimal estimation of the rotor speed and position is attained by minimizing the error between measured and estimated stator currents as a modified cost function, within a finite time horizon. The modified cost function includes the updation of kalman gain constant to enrich the estimation of speed variable even under noisy conditions. The proposed MHE based sensorless method is validated by performing speed control using cascaded PI based speed and current controllers for SynRM, in extensive fixed-step MATLAB simulation environment

This paper discusses the position sensorless control of a salient-pole synchronous-reluctance machine (SynRM) whose performance is analysed. Depending on the rotational speed of the machine different methods have to be applied. At low-speed range the INFORM method is used, which is based on the magnetic anisotropy of the rotor. At high-speed range two different methods are implemented and compared. The first method is the Back-EMF model, which is based on the flux estimation by integrating the induced voltage. The second method, the short-circuit method, directly uses the measured electrical quantities to calculate the rotor position. No further integration or other dynamical operations are necessary, which increases the dynamic of the position sensorless control model. These mentioned position sensorless control methods were implemented and tested on a prototype to verify the performance of each algorithm.

No abstract available

No abstract available

A dynamic inductance (DYI) model for sensorless control of Synchronous Reluctance Motor (SynRM) drive in low-speed regions is proposed. The model is analyzed in the abc frame and estimates the rotor position without using any mechanical sensors. The effectiveness of the DYI model in estimating the rotor position is validated through simulation results. The proposed DYI model addresses the challenges of rotor position estimation in low-speed regions.

No abstract available

The high-frequency injection (HFI) method is commonly used for sensorless control of synchronous reluctance motors (SynRMs) at low speeds. Injecting a high-frequency (HF) signal in a predefined direction, referred to as the <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula>-axis, and further demodulating the excited signal in the <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-axis are a popular sequence for SynRM position tracking. Injection in the <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-axis is not usually considered due to its potential to increase torque ripple. This article proposes a generalized HF pulsating voltage injection approach for sensorless control of SynRM. This approach allows for the arbitrary selection of injection strategy and covers the injection and demodulation procedures in <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula>- and <inline-formula><tex-math notation="LaTeX">$q$</tex-math></inline-formula>-axes in detail. Special attention is given to determining the amplitude of the injection voltage, which can extend the usage range of HFI and support sensorless control at high speeds. The effectiveness of the proposed structures is experimentally verified using 15 kW SynRM. During system validation, extra care is taken to investigate the overall system efficiency and vibration levels, including torque ripple issues. This solution contributes to the advancement of sensorless control for SynRM drives.

This work presents a hybrid biaxial pseudorandom high-frequency signal injection (Bi-PR-HFSI) strategy to suppress high-frequency (HF) torque ripple and electromagnetic (EM) vibration, thereby improving the low-speed sensorless control performance of SynRMs. The proposed method combines hybrid HF excitation with Bi-PR modulation, which helps redistribute spectral energy while suppressing discrete harmonic components in the HF current. Compared with conventional square-wave injection techniques, the injected hybrid voltage contributes to lower current components and minimizes HF torque ripple and EM vibration under injected frequencies. Despite the increased waveform complexity, the hybrid signal remains compatible with demodulation schemes, ensuring no additional computational overhead. Experimental evaluation on a 5.5 kW SynRM under fixed and PR injection conditions confirms that the hybrid voltage effectively smooths HF torque ripple and suppresses EM vibration across the injected frequency range. These results highlight the feasibility and effectiveness of the hybrid Bi-PR-HFSI method in improving motor performance for sensorless SynRM drives.

This work proposes a hybrid d-axis high-frequency signal injection (HFSI) technique for low-speed sensorless control of SynRMs, aiming to regulate the high-frequency (HF) current and suppress HF torque ripple and electromagnetic (EM) vibration. The proposed technique employs a hybrid sinusoidal excitation, which, under the same current amplitude, generates lower HF current and HF torque components than the conventional sinusoidal and square-wave HFSI techniques, as validated through power spectral density (PSD) analysis. Benefiting from its intrinsically lower HF injection noise characteristics, the hybrid signal further achieves reduced HF noise when combined with pseudo-random signal injection approach, thereby outperforming sinusoidal and square-wave pseudo-random signal injection methods. Although the injected hybrid HF signal exhibits a complex waveform, this complexity is effectively managed during signal demodulation without increasing the computational burden of the sensorless control algorithm. Experimental tests using both fixed-frequency and pseudo-random d-axis HFSI approaches with sinusoidal, hybrid and square-wave signals on a 5.5 kW SynRM demonstrate that the proposed hybrid signal significantly mitigates HF torque ripple and EM vibrations, thereby enhancing the sensorless control performance of the SynRMs.

Model predictive current control (MPCC) has become one of the most promising control strategies for synchronous reluctance motor (SynRM) drive system. However, the parameters sensitivity is still a bottleneck problem for MPC, and the existing methods are not suitable for sensorless control. This paper proposes a sensorless MPCC strategy based on alternative high-frequency square-wave voltage injection (AHFSVI). The proposed methods inject an alternative high-frequency signals on dq-axis of the stator voltage, estimating rotor position, rotor speed and inductance simultaneously. By updating the inductance used in MPC per injection cycle, the proposed method can effectively suppress the impact caused by parameter mismatches. Finally, the effectiveness of the proposed method is verified by Simulink.

In order to achieve the high-performance control position sensorless vector control of synchronous reluctance motor (SynRM), this paper investigated its running-up performance and capability at zero or low speed range. The contribution of this paper mainly includes two parts. Firstly, it takes cross-saturation effect into consideration, Secondly, the paper improves the control stiffness and reliability under low-speed sensorless operation condition by adopting the pulsating sinusoidal high frequency voltage injection snesorless method. By injecting high-frequency voltage excitation signal, the rotor position is estimated from the high-frequency response current. The simulation results verified the feasibility of the control algorithm, and fulfilled the running-up of the SynRM at zero speed, the capability of stability is evaluated via simulation results.

Synchronous Reluctance Motor (SynRM) has large magnetic saturation, and it experiences fluctuations in parameters such as flux and inductance because of magnetic saturation as the amount of current passing through it changes. Because of this, when saliency based sensorless control method is used for position estimation, the characteristic for position estimation becomes nonlinear. In this case, the accuracy of position estimation may decrease, or the controlled motor may lose synchronization. To solve this problem, the characteristics arising from the high-frequency square wave injection method is modeled including the cross-coupling effect and a method for combining two characteristics on dq-axes from the model equation is proposed in this paper. Then, by deriving characteristics from the result of electromagnetic field analysis using Finite Element Analysis (FEA) and theoretical equation, it is possible to verify their validity from the comparison results with the characteristics obtained from the actual machine. In the end, the proposed method is verified by experiments on a 2.2-kW SynRM drive platform.

There are usually noise problems when the position sensorless control of a synchronous reluctance motor (SynRM) is carried out by high-frequency (HF) signal injection method. Due to the special structure, the cross-saturation effect of the SynRM is particularly serious, resulting in reduced position observation accuracy. In this paper, a variable-angle random HF voltage injection strategy with cross-saturation effect compensation is proposed for position sensorless SynRM drives. Random number generation based on the chaotic mapping method is used to generate random HF voltage signals with different frequencies for injection; the current power spectral density (PSD) distribution is extended and the HF noise can be reduced. A control strategy based on variable-angle square-wave injection is proposed to suppress the cross-saturation effect. By measuring the position error curves of different loads off-line and polynomial fitting the curves, the position error is achieved by combining with the corresponding signal demodulation algorithm. The proposed method does not require additional hardware resources and can maintain high control accuracy and robustness. Finally, the effectiveness of the proposed sensorless control strategy is verified on a 3 kW SynRM experimental platform.

Aiming at the nonlinear characteristics of synchronous reluctance motor (SynRM) caused by electromagnetic saturation and cross coupling of stator inductance, a sensorless control method combining recursive least square method and pulsating high frequency current injection was proposed. The principle is to inject high frequency sinusoidal current signals with fixed amplitude into the direct axis of synchronous reluctance motor observation coordinate system, and extract voltage signals containing rotor position information from the quadrature axis. The recursive least square method with forgetting factor is used to realize the online identification of the motor resistance and inductance parameters. The sensorless control of motor is realized by proper signal processing method. In this paper, theoretical analysis, software simulation and prototype experiments are carried out to verify the accuracy and practicability of the proposed method.

No abstract available

The main limitation for the wide application of synchronous reluctance motors (SynRMs) is the torque ripple caused by the special structures and control method. To lower the cost of SynRM drive system and expand speed range of SynRMs, high frequency signal injection (HFSI) is an effective sensorless control scheme for SynRMs to achieve low and zero speed operation. For the sensorless control, the low frequency torque ripple caused by asymmetric phase currents has been studied by some researchers, however, the high frequency torque ripple and the audible noise produced by it is mostly neglected and needs further investigation. This paper proposes a novel scheme for HFSI method to acquire accurate rotor position information and suppress the high frequency torque ripple caused by high frequency signals at the same time. Applying the special injection scheme, high frequency torque ripple suppression is achieved. The effectiveness of the proposed method is verified in test-bench experiments.

The precision of motor parameters directly affects the control performance of SynRMs drive system. Currently, high-frequency injection methods often generate noise and are unsuitable for high-power and low carrier wave ratio scenarios. Meanwhile, model-based methods can only identify the saturation characteristics of the <italic>L_d</italic> without calibration bench loading. This letter proposes an identification method based on a novel active flux observer orientation, which does not require high-frequency injection or calibration bench loading to identify the saturation characteristics of <italic>dq</italic>-axis inductance. During identification <italic>L_d</italic>, the active flux observer adopts <italic>d</italic>-axis orientation and identifies the saturation values of <italic>L_d</italic> by applying different <italic>i_d</italic>. When identifying the <italic>L_q</italic>, the active flux observer adopts negative <italic>q</italic>-axis orientation, and the output of the speed loop is used as the input for the <italic>d</italic>-axis current loop. By applying different <italic>i_q</italic>, the saturation values of <italic>L_q</italic> are identified. Experimental results show the validity of the proposed method.

The sensorless control of synchronous reluctance motor (SynRM) based on high-frequency signal injection is susceptible to position estimation errors caused by the cross-saturation effect. Higher loads intensify cross saturation, resulting in increased position estimation errors and deterioration in control performance. This paper proposes an offline identification method for SynRM cross saturation angle based on the dual-axis high-frequency square wave voltage injection (DHFSVI) technique to enhance the sensorless control performance of SynRM at low speeds. Following that, the compensation is performed using the improved normalized cross saturation angle compensation (INCCAC) method. The compensation algorithm effectively identifies and compensates for position estimation errors induced by cross saturation, thereby enhancing the control performance of SynRM sensorless control at low-speed.

Torque smoothness is a strict requirement for synchronous reluctance motor (SynRM) industrial applications. The speed fluctuations induced by the high-frequency and low-frequency torque ripples deteriorate the drive performance. To suppress high-frequency torque ripples chronically omitted by conventional high-frequency signals injection methods at low-speed operation, this article proposes a bi-axes high-frequency signals injection (Bi-HFSI) scheme. A novel signal injection method is adopted, and the torque power spectrum can be broadened with discrete peaks effectively suppressed. In addition, aiming at decreasing low-frequency torque ripples, the Fourier iterative learning control algorithm that is effective in suppressing periodic disturbance is applied. It is combined with the Bi-HFSI scheme which mainly limits torque ripples at low frequencies including 1st, 2nd, and 6th harmonics. Theoretical analysis is presented, and the proposed method is verified by experiments on a 3-kW SynRM drive platform.

This study investigated the effect of an inductance model on the sensorless control performance of a synchronous reluctance motor (SynRM). For sensorless control, a high-frequency voltage injection method based on the position dependence of the inductance and a extended electromotive force estimation method based on the motor model were applied. In this study, the inductances were modeled in several patterns and applied to the two methods. Then, the characteristics in the steady and transient states were compared. Also, by comparing the characteristics in the full-speed region by combining the two methods, the effect of the inductance model on the sensorless control performance of a SynRM with magnetic saturation was investigated.

This article presents a model of a Synchronous Reluctance Motor (SynRM) at standstill, including magnetic hysteresis, to study its impact on High-Frequency (HF) signal injection. So that it can easily be simulated, the model is rewritten under state-form. The parameters of the model are identified, based on experimental data, using a customized fitting procedure adapted to hysteresis curves. The proposed model explains the impact of magnetic hysteresis on HF signal injection and reproduces accurately the experiments.

No abstract available

No abstract available

This paper presents a predictive strategy and Model Reference Adaptive System (MRAS) technique for controlling the synchronous reluctance motor (SynRM). A number of sensorless control methods have been developed such as extended Kalman filter (EKF), high-frequency signal injection methods and extended electromotive force (EEMF) models considering magnetic saturation. The aim of our approach is to estimate the rotor speed by the MRAS estimator and then submit this estimate to the Model predictive control. Thus, to obtain a predictive control, the most idea is to extract the variables to be controlled from the model of the machine to calculate their next behavior and to choose the best optimization criterion. In order to feed the predictive control model and reach the optimal speed, it is necessary to subject it to the model reference adaptive system, which employs the state observer model with current error feedback and the rotor current model as two models for current estimation. Thus, to extract the estimated speed, the difference between the outputs of the state observer model and the rotor current model is used to obtain a zero error.

The most distinctive feature of synchronous reluctance motor (SynRM) is self-saturation and cross-saturation, which results in a lack of physically consistent guidelines for an accurate analytical model with current as an independent variable. In this article, an analytical reciprocity magnetic model (RMM) is proposed to describe the nonlinear magnetic behaviors of SynRM. In the proposed model, the physical constraints are obtained by the analysis of the excitation level in the self-axis and the energy conservation relationship between the orthogonal windings. Then, based on the irrotationality of flux linkage in the coupling field, a modified Gaussian function is constructed to describe the saturation and magnetic coenergy. Furthermore, the advantages of the proposed model are verified through the proposed dual-axis hybrid excited self-commissioning method and the two-stage collaborative coupling (TC) algorithm. In comparison with other modeling techniques, the proposed RMM shows superior comprehensive performance. The experimental results demonstrate that the proposed model has high accuracy and availability.

This paper proposes a magnetic flux saturation model that well represents the cross saturation of SynRM and a parameter estimation method of the proposed saturation model. The proposed flux saturation model consists of terms for self-saturation and cross-saturation, and it expresses well the nonlinear relationship between current and flux of SynRMs, as well as satisfies the reciprocity condition. Using the flux saturation data obtained during self-identification, the parameters of the flux saturation model are estimated. Because the proposed magnetic flux saturation model includes an arctangent function, it is not possible to estimate parameters directly using linear least squares method. However, the proposed parameter estimation method integrates the self-saturation model and transforms it into a polynomial to which linear LSM can be applied. In addition, parameters related to cross saturation are also estimated using linear LSM. Therefore, the proposed parameter estimation method is easy to implement and can be applied to general-purposed inverter products. The effectiveness of the proposed model and its identification method were experimentally evaluated with a 1.5 kW SynRM. Additionally, the identified model was verified with the accuracy of the MTPA table and the performance of sensorless control.

For synchronous reluctance motor (SynRM) parameters are easy to change when the magnetic circuit is saturated, and the characteristics of significant nonlinearity, the control based on fixed parameters will easily lead to poor control accuracy. In this paper, the influence of magnetic saturation of the motor magnetic circuit on the parameters is taken into account, and the motor parameters based on the stationary identification technique are identified offline. Simultaneously, due to the effect of hysteresis saturation, it is difficult to simply fit the stationary identified magnetic flux linkage parameters into the motor parameter curves. This paper combines a neural network training model with a control algorithm to dynamically assign the optimal current in real-time to improve response speed and robustness when controlling synchronous reluctance motors. By applying the neural network training data to the simulation analysis of MTPA control, the effectiveness of the adopted identification method and motor modeling method as well as the identified parameters applied to the high-precision control of synchronous reluctance motors is verified.

For synchronous reluctance motor (SynRM), parameters tends to change when the magnetic is saturated, and the characteristics are significantly nonlinear. The conventional maximum torque per ampere (MTPA) control of SynRM, which ignores the deviation terms, is subject to significant errors about current angle and leads to torque degradation. For the saturation of magnetic, relying only on finite element simulation, the accuracy of motor parameters is still not guaranteed in practical applications. In this study, an online approach of inductance parameters determination that can account for magnetic self-saturation and cross-saturation was proposed. To achieve high-precision maximum torque per ampere (MTPA) control of SynRM concerning torque degradation, this article first analyzes the factors contributing to the MTPA control error of SynRM. It then combines the identification of SynRM inductance parameters to compensate for the deviation terms online and applies the virtual signal injection method. Simulation and experimental results verify the effectiveness of the proposed parameter identification approach and high-precision MTPA control strategy considering deviation terms.

The synchronous reluctance machine is well-known for its highly nonlinear magnetic saturation and cross-saturation characteristics. For high performance and high-efficiency control, the flux-linkage maps and maximum torque per ampere table are of paramount importance. This study proposes a novel automated online searching method for obtaining accurate flux-linkage and maximum torque per ampere Identification. A limited 6 × 2 dq-axis flux-linkage look-up table is acquired by applying symmetric triangle pulses during the self-commissioning stage. Then, three three-dimensional modified linear cubic spline interpolation methods are applied to extend the flux-linkage map. The proposed golden section searching method can be easily implemented to realize higher maximum torque per ampere accuracy after 11 iterations with a standard drive, which is a proven faster solution with reduced memory sources occupied. The proposed algorithm is verified and tested on a 15-kW SynRM drive. Furthermore, the iterative and execution times are evaluated.

In the process of operation, the parameters of Synchronous Reluctance Motor (SynRM) will be changed by such factors as temperature and magnetic saturation, so as to reduce the control performance. Therefore, obtaining accurate motor parameters in high-performance control is of great significance. To solve the problem of the inductance of alternating and straight axis varies greatly during the operation of SynRM, an improved model reference adaptive online inductance identification method of SynRM is proposed in this paper. Firstly, the basic principle of inductance parameters online identification in model reference adaptive system (MRAS) is studied, and the variable forgetting factor robust recursive least square (VFFRRLS) method is introduced into MRAS algorithm. Based on the inductance parameters identification values at the current time, the second identification is carried out and the adjustable model is updated in real time to improve the speed of MRAS inductance parameters online identification and the anti-disturbance performance of the identification process. Finally, the proposed method is applied for the experimental verification, and the performance is compared with that of the traditional method in inductance parameters online identification under the different operating conditions, which validates the correctness and effectiveness of the proposed method.

Recently, model-based predictive current control (MB-PCC) has been presented as a good alternative to classical control algorithms in terms of simplicity and performance reliability. However, MB-PCC suffers from the high dependence on system parameters, which may deteriorate its performance under parameters variations. On the other hand, synchronous reluctance motors (SynRMs) are susceptible to suffer from inductances variations due to the magnetic saturation. Accordingly, in this article a new model-free predictive current control of SynRMs based on a recurrent neural network (RNN-PCC) is developed and proposed. The proposed RNN-PCC relies on the identification of the SynRM currents without considering any parameters. Simulation and experimental results show that both RNN-PCC and MB-PCC have similarly excellent dynamics, while better control performance and tracking errors can be achieved thanks to the proposed RNN-PCC.

No abstract available

This article proposes a novel unified optimal control method (UOCM) for synchronous reluctance motor (SynRM) considering magnetic saturation. The conventional methods require operating case classification based on torque and speed commands, which is complex and computationally intensive. Moreover, the conventional methods only obtain the local optimal current reference in the maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) cases, even though the parameter variations due to magnetic saturation are considered. This article proposes a unified optimization criterion for different operating cases to avoid case classification. The proposed criterion is essentially an optimization problem on the torque curve, thus ensuring the ability to obtain the global optimal current reference even when the motor is deeply saturated. A finite current angle set-based search method (FCAS-SM) is proposed to determine the optimal current reference online, which is equipped with offline precomputed lookup tables (LUTs) to reduce the computational effort. Moreover, the proposed method ensures that the obtained current references are always attainable by dynamically limiting the torque command through a simple integral controller. Experiments are carried out on a 3-kW SynRM platform to verify the performance of the proposed method in full-speed and torque regions.

Maximum torque per ampere (MTPA) control is widely used for synchronous reluctance motors (SynRMs) at low speeds to maximize efficiency. Considering the magnetic saturation effect, i.e., the inductance or flux parameters vary with the current operating point, determining the MTPA operating point requires the parameter information instantly extracted from the premeasured look-up tables (LuTs). However, the conventional method only determines the local optimal MTPA operating point related to the specific parameter information rather than the global optimal point. Therefore, this paper proposes a model-based online search method for global optimal MTPA. Firstly, the global optimal MTPA criterion is defined. Then, an iterative algorithm based on a finite current angle set is proposed to trade off the accuracy and computational burden of optimal operating point searching. The experimental comparison on a 3-kW SynRM platform verified the effectiveness of the proposed method.

The main feature of synchronous reluctance machine (SynRM) is highly nonlinearity for flux-linkage, due to magnetic saturation and cross-saturation effect. Maximum torque per ampere (MTPA) control focuses on minimizing copper loss, which is a popular and important method for SynRM high-efficiency control. In this study, an improved golden section searching method (GSSM) is proposed for fast MTPA acquisition, taking into account nonlinear magnetic saturation and cross-saturation effect. Compared with traditional GSSM, the key feature is to use the MTPA optimal angle from the last iteration to optimize the initial search interval, thereby improving the system convergence speed and reducing computing resources. The proposed GSSM can be easily implemented with several iterations with a standard drive. The proposed algorithm has been implemented by a digital signal processor (DSP)-based 15-kW SynRM variable frequency converter.

This article proposes a reliable measurement methodology for high-efficiency line-start synchronous reluctance motors (LS-SynRMs), focusing on the measurement and analysis of phase inductance across various rotor positions and different supplied current values. The proposed method is valid for synchronous machines (SMs), and the LS-SynRM is used as an example of its verification. The investigation is motivated by the increasing demand for improved performance and efficiency in electromechanical systems, particularly in applications where the operating characteristics of a motor can be improved by proper control adjustment based on the actual motor inductance. Also, the actual accurate measured inductace of a newly developed motor can be used as a feedback for further motor design optimization. To address this, a novel methodology is introduced for evaluating the inductance based on flux linkage measurements. This approach incorporates a comprehensive uncertainty analysis, which enhances the reliability and accuracy of the results by accounting for various sources of measurement error. Experimental investigations were conducted to assess the inductance behavior of the LS-SynRM under different current levels. The results reveal significant effects of magnetic saturation on inductance variation, with measurements indicating a notable range of inductance values. Each measurement is accompanied by associated uncertainties, providing a deeper understanding of how inductance fluctuates with rotor position and current levels. By highlighting the relationship between inductance, rotor position, and measurement uncertainty, this research contributes valuable insights that can inform the design and optimization of future motor systems.

This article aims to provide a more detailed analysis and optimization of the implementation process of a recently proposed magnetic saturation model identification method for synchronous reluctance motors. This model identification method uses injection voltages and ensures that the motor rotor remains stationary without locking during the experimental process. The data are processed using the least-squares method. The magnetic saturation model includes both self-saturation and cross-saturation parts, which conform to the actual motor modeling requirements. It seems that most model identification tests that use injection voltage ignore the impact of deadtime effects. This can lead to incorrect magnetic flux calculation, so a compensation method is proposed in this article. Furthermore, due to the need to ensure that the injection voltage direction is correct, keeping the rotor standstill during the testing process is necessary. It is difficult to select the injected voltage value during the cross-saturation model parameters identification testing process, and there is no absolutely correct configuration method for setup the values. This article will analyze this and propose more reasonable limiting conditions. Finally, a 5-kW synchronous reluctance motor is identified through simulation and practical operation, and the magnetic saturation model of the motor is obtained.

This article proposes a parameter estimation technique for a self-saturation model of synchronous motors. The standstill self-identification method requires an equation-based flux saturation model for flux saturation modeling. The selected self-saturation model has the form of a polynomial representing a current with respect to a magnetic flux. This flux saturation model contains unknown parameters, including exponents, coefficients, and a current source for a permanent magnet. The exponent is a crucial parameter representing the nonlinear current and magnetic flux relationship. Therefore, the estimated flux saturation model only represents the flux saturation well if an appropriate exponent is selected. The proposed method can estimate the exponent of the self-saturation model for synchronous reluctance motors (SynRMs) using a linear least squares method (LSM). In addition, parameters of the self-saturation model for permanent magnet synchronous motors (PMSMs), including the exponents and a constant current source, can be estimated simultaneously. The rest of the coefficients of the flux saturation model are estimated using a linear LSM based on the pre-estimated exponents and a constant current source. The proposed parameter estimation method can be implemented in embedded systems. The performance of the proposed parameter estimation method was verified in 1.5-kW SynRM and 11-kW interior PMSM.

The synchronous reluctance motor (SynRM) has significant nonlinear characteristics due to the problems of magnetic saturation and cross-coupling and the poor adaptability of the general controller to parameter changes seriously affects the control performance of the motor. In order to solve the above problems, this paper proposed a control system for the SynRM with a brain emotion controller based on empirical information to solve the motor control problem of magnetic saturation. Firstly, the nonlinear mathematical model of the SynRM considering magnetic saturation is established by introducing the magnetic saturation parameter. Secondly, the sensory input function and emotional cue function based on systematic error are given and the vector control system of the SynRM considering magnetic saturation is designed. The influence of the parameters and the learning rate of the brain emotional learning based intelligent controller (EI-BELBIC) on the adjustment range of the controller parameters is studied. Then the SynRM is controlled under different working conditions and the control effect is observed. The results show that the designed vector control system of the SynRM based on EI-BELBIC has strong reliability, accurate control, rapid response, and strong anti-interference ability under magnetic saturation.

The inductance of dual three-phase synchronous reluctance motor (DTP-SynRM) is not only an important factor affecting its torque and power factor, but also determines the quality of the motor. In order to solve the problem of poor control accuracy of DTP-SynRM in practical operation due to magnetic saturation, an improved equivalent excitation current method is proposed for the first time, which can more accurately characterize the influence of main magnetic circuit saturation and cross saturation on the inductance ($L_{\mathrm{d}},\ L_{\mathrm{q}}$) of D-axis and Q-axis. Firstly, the degree of magnetic saturation of $L_{\mathrm{d}}$ and $L_{\mathrm{q}}$ of the motor is analyzed by finite element method. Then, the influence of self-saturation and cross-saturation of main magnetic circuit on $L_{\mathrm{d}}$ and $L_{\mathrm{q}}$ was characterized by two kinds of equivalent excitation currents and saturation coefficients. Finally, a nonlinear simulation model considering magnetic saturation of DTP-SynRM field oriented control is built, and the hardware platform of the control system is designed. The correctness of the method is verified by simulation and experiment results.

Operation of electric drives in the overmodulation region and six-step can optimize switching losses and increase the utilization of the dc-link voltage, but inevitably introduces low-order voltage harmonics that appear as current distortions in the stator winding. These harmonics adversely affect the field-oriented control loops, which in theory operate on fundamental components only. This paper presents an analytical method for mitigating low-order current harmonics in a feedback path of overmodulated Synchronous Reluctance Machine (SynRM) drives controlled under a combined Maximum Torque Per Ampere (MTPA) and Field-Weakening (FW) strategy. The proposed algorithm analytically estimates the 5th, 7th, 11th, and 13th harmonic components using explicit equations and synthesizes compensating current vectors using a machine model with differential inductances that accurately capture magnetic saturation and cross-saturation. Simulation and experimental results obtained on an 11kW SynRM drive confirm a substantial reduction in the $d$ - and $q$ -axis current ripple in the feedback path without deteriorating dynamic performance. The proposed approach enables smooth operation up to the six-step mode and provides a robust foundation for future observer-based or sensorless control schemes that rely chiefly on fundamental current and voltage components.

This paper proposes a method for estimating the magnetic flux saturation model of SynRM in a stationary state using an Artificial Neural Network (ANN). In the stationary state, the ANN is trained using the sampled current and the calculated magnetic flux obtained during hysteresis current control. The d-q axis magnetic flux generated according to the d-q axis current of SynRM appears symmetrically with respect to the axis and the origin. Using this phenomenon, the model was trained in the first quadrant by taking absolute values from the current and magnetic flux data. It was confirmed that the trained ANN model can represent the magnetic flux saturation phenomenon by comparing the estimated magnetic flux of the ANN model with the current-flux data. To verify the effectiveness of the proposed methods, the ANN flux saturation model was applied to sensorless drives with 1.5kW SynRM.

Voltage feedback closed-loop control is commonly adopted in Interior Permanent Magnet (IPM) and Synchronous Reluctance (SynRM) motor drives in the flux-weakening (FW) range to comply with inverter voltage limitation. Analysis of voltage control loop transfer function was reported for the first time in [1] [2], demonstrating its non-linear behavior. Adaptive regulation approach was also proposed to allow optimization of regulation dynamics and maximization of DC bus voltage utilization. The linearization methods proposed in [1]–[3] are not suitable for the Maximum-Torque-per-Voltage (MTPV) range. Extension to MTPV range employing a modified flux-weakening model of the analysis methodology was later proposed in [4] but lacking a theoretical analysis of non-linear machines. The theoretical contribution of this paper allows to extend the adaptive control to the full speed range, even in the case of non-linear machines, which was never considered in any previous paper. Faster control and reduced voltage margin are possible, leading to higher torque capability across the whole speed range. Accurate theoretical analysis and simulation results are reported to prove the effectiveness of the proposal.

In sensorless control of Synchronous Reluctance Motor (SynRM), the traditional Model Reference Adaptive System (MRAS) linear compensator is limited by motor parameters, which results in poor control effect. To overcome this limitation and achieve better position sensorless vector control of SynRM, a new linear compensator has been designed. Additionally, a new speed adaptive law has been derived based on Popov's theory of superstability, and a new MRAS has been designed. The designed MRAS was verified through simulation and experiment. The results demonstrate that the system is both feasible and effective, accurately identifying motor speed and rotor position information with good performance.

The sensorless application of predictive control in drive applications has been investigated for a decade. Finite control set model predictive control (FCS-MPC) is one of the easy and practical methods in the predictive category. Several methods have been investigated for the sensorless application of FCS-MPC. Since the sensitivity of the predictive method to the speed error is more than that of the classical control methods, sophisticated speed estimators should be used in this method. The model reference adaptive system (MRAS) has been the most successful estimator. The main problem of this estimator is tuning the coefficients in different operating points and the stability of the adaptive function. The finite position set technique is a very recent solution. In this method, the adaptive function is used as the cost function and the optimum rotor position is selected by minimizing that. However, the numerous iteration is a barrier for application to the predictive method. Also, the application of the method for the synchronous reluctance motor (SynRM) is a challenge because of the lack of the rotor model as the adaptive function. In this paper, the finite position technique is modified for the predictive application. The number of iterations is reduced by an optimization method based on sensitivity analysis. Also, a new and simple function is used as the adaptive error function in order to apply the method to the sensorless control of the SynRM. The proposed method is evaluated by simulation and experiment.

This paper presents a rotor speed estimation technique based on a Luenberger observer for sensorless speed control of a synchronous reluctance motor (SynRM). The model of the SynRM is presented, both on the synchronous rotating frame as well as in the stationary rotating frame. The observer design is presented, along with the extended state space model, used to obtain both actual and observed αß currents and αß electromotive force (EMF). The field oriented control structure used in this paper is shown. Lastly, experimental results are presented in order to verify the effectiveness of the sensorless control strategy along with the convergence of the observed variables and estimated speed.

No abstract available

Direct torque control (DTC) method controls the electromagnetic torque and flux in AC machines, directly. This method is though immune to machine inductance variations but do get affected by stator resistance changes. Ignoring the stator resistance variation can lead to unstable motor drive operation. This paper proposes a new sensorless direct torque control (sDTC) method for a Synchronous reluctance motor that adapts to stator resistance variation. This is accomplished by adapting to changes in the error between the estimated and reference angles of the current and the stator flux vectors. Importantly, it completely obviates the need of any speed sensor. With the present estimation proposal, the sDTC is made to dynamically adjust to the changes in stator resistance ensuring proper motor performance under different operating conditions involving disturbances. The proposed sDTC approach manages to improves the torque and actual stator flux in the SynRm unlike existing DTC approach that greatly struggles under stator resistance variations and results in unstable operation. The performance of the SynRm with the envisaged sDTC with stator resistance change proposed in this paper is compared with the existing DTC method for a close one-to-one comparison and easy appreciation. Besides, ruggedness of the proposed approach for the SynRm is tested under stator resistance changes, motor speed changes and load disturbances.