Control Of Synchronous Reluctance Motor Research Articles

Fuzzy Logic Based DTC Control of Synchronous Reluctance Motor

Fuzzy Logic Based DTC Control of Synchronous Reluctance Motor

Robust Sensorless Control of Synchronous Reluctance Motor Based on PDSH-KF Considering Dynamic Magnetic Saturation Effect

Robust Sensorless Control of Synchronous Reluctance Motor Based on PDSH-KF Considering Dynamic Magnetic Saturation Effect

Improved Triple-Voltage-Vector Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Improved Triple-Voltage-Vector Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Robust Control of Synchronous Reluctance Motor Based on Automatic Disturbance Rejection

Robust Control of Synchronous Reluctance Motor Based on Automatic Disturbance Rejection

Finite-time robust speed control of synchronous reluctance motor using disturbance rejection sliding mode control with advanced reaching law.

In recent years, there has been a significant focus on synchronous reluctance motors (SynRM) owing to their impressive efficiency and absence of magnetic material. Although the SynRM shows great potential for use in electric vehicles, its widespread adoption is limited by unmodeled dynamics and external disturbances. Moreover, the uncertainty factor significantly restricts SynRM's peak efficiency and superior control performance, leading to an unjustifiable current loop reference command. To address these issues, this work presents various new research contributions which focus on the robust control of SynRM to optimize performance through the novel reaching law-based sliding mode control. Initially, a novel advanced sliding mode control reaching law (ASMCRL) with adaptive gain is proposed, to enhance the acceleration of the system state reaching the sliding surface. After that, an extended state observer (ESO) is designed to estimate and compensate for the overall disturbances of the system. Finally, the ASMCRL and ESO are integrated to design two nonlinear controllers namely, the disturbance-rejection sliding mode controller (DRSMC) and the disturbance-rejection sliding mode speed regulator (DRSMSR) for SynRM. The proposed DRSMSR eliminates the steady-state error and eradicates inherent chattering in DRSMC. Moreover, this yields a system trajectory that converges to a predetermined proximity of the sliding surface, irrespective of any lumped disturbances. The steady-state error of DRSMSR is less as compared to DRSMC. Furthermore, the speed response of this technique is 22.62% faster as compared to the state-of-the-art finite-time adaptive terminal sliding mode control. Additionally, the asymptotic stability of the proposed system is validated using Lyapunov's theorem. Thus the experimental results demonstrate the effectiveness and robustness of the proposed approach.

Proximate Maximum Efficiency Control for Synchronous Reluctance Motor via AMRCT and MTPA Control

To develop a high-efficiency synchronous reluctance motor (SynRM) drive system, a proximate maximum efficiency (PME) control is proposed in this study. First, a SynRM drive based on a traditional vector control with a constant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis current command is developed. However, the constant command is not suitable for the high-efficiency applications of SynRM. Therefore, a three-control-mode system including the adjustable maximum rate of change of torque (AMRCT) control, finite element analysis (FEA)-type maximum torque per ampere (MTPA) control, and PME control is proposed to enhance the efficiency of the SynRM drive system. Moreover, the PME control is a combination of an AMRCT control and an FEA-type MTPA control. As a result, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis current command is flexible and depends on the minimizing loss target chosen by the end-user. In addition, a novel adaptive computed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis current speed control with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -axis current regulation is proposed to further increase the robustness of speed control. Finally, the proposed three-control-mode system is implemented in a 32-bit floating-point digital signal processor and some experimental results are provided to verify its effectiveness.

Development of an Advanced AC Drive for the Heating Circulation Pumps with Dry-Rotor Using a Synchronous Reluctance Motor

This study presents an enhanced AC drive for the SynRMs coupled with a circulation pump (CP) and an improved control system based on the field orientation. A grid-fed 900 W motor driver consisting of an electromagnetic interference filter (EMI), power factor correction (PFC), and three-phase inverter is designed to drive 0.75 kW SynRM. A boost PFC converter is designed as an interface between the AC power line and the motor drive. PFC is used to recover the bad power factor performance of the SynRM. In addition, the boosted and well-regulated DC-link voltage improves the motor driving performance. Control strategies and practical implementation based on the field-oriented control of synchronous reluctance motors are presented. Dynamic and steady-state responses of the CP SynRM drive in various conditions are verified experimentally. Low current and voltage ripples, unity power factor, maximum torque per ampere with low fluctuation and low EMI noise are achieved with the low cost efficiently. All of the improvements are obtained by fewer circuit elements and higher energy conversion efficiency. The advantages of using SynRMs in dry-rotor circulation pumps for heating applications have been demonstrated experimentally.

Model-free control-based vector control of synchronous reluctance motor

Model-free control-based vector control of synchronous reluctance motor

Sensorless Speed Control of Synchronous Reluctance Motor Drives Based on Extended Kalman Filter and Neural Magnetic Model

Due to their robustness and adaptability, position estimators based on the extended Kalman filter have been used in permanent magnet synchronous motors for decades. The time has come to extend their use to reluctance motors as well and this work focuses on the elements that hinder the transition. All passes through the availability of an accurate and analytical magnetic model, which is obtained by Artificial Intelligence tools. It is proved that the sensorless control of synchronous reluctance motors using the extended Kalman filter is possible over broad speed and torque ranges. The experimental session compares different implementation possibilities, concluding with the proposal of a new hybrid algorithm that greatly reduces the computational load.

Direct Model Predictive Control of Synchronous Reluctance Motor Drives

This paper investigates a finite-control set model-predictive control (FCS-MPC) algorithm to enhance the performance of a synchronous reluctance machine drive. Particular emphasis is placed on the definition of the cost function enabling a computationally light implementation while targeting good transient and steady-state performance. In particular, this work proposes the inclusion of an integral term into the cost function to ensure zero steady-state errors thus compensating for any model inaccuracies. A control effort term is also considered in the formulation of the cost function to achieve a high ratio between the sampling frequency and the average switching frequency. After a comprehensive simulation study showing the advantages of the proposed approach over the conventional FCS-MPC for a wide range of operating conditions, several experimental test results are reported. The effectiveness of the proposed control approach, including a detailed analysis of the effect of the load and speed variations, is thus fully verified providing useful guidelines for the design of a direct model predictive controller of synchronous reluctance motor drives.

A Lag Compensation-Enhanced Adaptive Quasi-Fading Kalman Filter for Sensorless Control of Synchronous Reluctance Motor

A novel position estimation strategy based on lag compensation-assisted adaptive quasi-fading Kalman filter (LC-AQFKF) is proposed for synchronous reluctance motor (SynRM) sensorless drive in this article. In LC-QFKF, the quasi-fading factor is derived to avoid the harsh assumptions of conventional adaptive fading Kalman filter, and accessibility of the method is improved while ensuring the estimation accuracy. The computational efficiency of LC-AQFKF is promoted by introducing the quasi-fading factor into the prediction error covariance matrix. Moreover, the frequency characteristic of AQFKF-based active back electromotive force observer is analyzed, and the phase lag problem in high-speed situations caused by the low-pass filtering property of AQFKF is overcome. In this way, the estimation accuracy of the rotor position is significantly enhanced. Besides, the double dynamic position compensation method is put forward to strengthen the position estimation performance of sensorless SynRM drive under dynamic conditions. The effectiveness of the proposed scheme is validated at a 1.5 kW SynRM drive.

Model-Free Predictive Current Control of Synchronous Reluctance Motors Based on a Recurrent Neural Network

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.

A Computationally Efficient Model Predictive Current Control of Synchronous Reluctance Motors Based on Hysteresis Comparators

Model predictive current control (MPCC) has recently become a powerful advanced control technology in industrial drives. However, current prediction in MPCC requires a high number of voltage vectors (VVs) synthesizable by the converter, thus being computationally demanding. Accordingly, in this paper, a computationally efficient MPCC of synchronous reluctance motors (SynRMs) that reduces the number of VVs used for prediction is proposed. By making the most of the simplicity of hysteresis current control (HCC) and integrating it with the MPCC scheme, only four out of eight predictions are needed to determine the best VV, dramatically reducing algorithm computations. The experimental results show that the execution time can be shortened by 20% while maintaining the highest control efficiency.

Sensorless Control of Synchronous Reluctance Motor Based on Active Flux Vector and Extended Kalman Filter

Sensorless Control of Synchronous Reluctance Motor Based on Active Flux Vector and Extended Kalman Filter

Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications

The gains of PI controllers, used in the cascaded speed control of synchronous reluctance motors (SynRMs), are synthesized using quantitative feedback theory (QFT). A systematic design approach is employed to quantitatively determine the PI controller gains in terms of speed and current loops, using a mathematical model of the SynRM. Further, to make the QFT design a more transparent method, an analytical procedure using the frequency domain is attempted to design the QFT bounds as well as the initial search space of the optimization algorithm used in automatic loop shaping. The effectiveness of the proposed PI tuning method is verified with the extensive MATLAB/Simulink simulation environment. The results illustrate the supremacy of the proposed PI tuning method, in terms of control performance, over the conventional PI tuning method, using the analytical procedure.

Research on Position Sensorless Vector Control of Synchronous Reluctance Motor Based on High Frequency Injection Method

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.

Modulated Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drives With Online Duty Cycle Calculation

This article proposes a modulated dual-voltage-vector model-free predictive current control with online duty cycle calculation, which is used to drive synchronous reluctance motors. The dual-voltage-vector modulation scheme reduces the current ripples and current errors present in the single-voltage-vector. The proposed method reduces the predictive current controller’s calculation time by first setting the initial value of the duty cycle to a constant. Then, an optimal switching mode can be selected by minimizing a cost function. Next, the required duty cycle can be calculated directly by the proposed method without any differential calculations instead of its initial value. The proposed method can effectively track the current, reducing the maximum average current error by 34.8% compared to its conventional single-voltage-vector one. Finally, the correctness and feasibility of the modulating model-free predictive current controller with the online duty cycle calculation proposed in this article are verified by the experimental results using Texas Instruments micro-controller TMS320F28379D.

Implementation of sensorless speed control of synchronous reluctance motor using extended Kalman filter

This paper presents an extended Kalman filter (EKF) based sensorless speed control which was performed for controlling a synchronous reluctance motor (SynRM). Maximum torque per ampere (MTPA) control strategy was used to obtain a maximum electromagnetic torque with lowest current for the motor. In this study, all control parameters were optimized using a particle swarm optimization (PSO) algorithm to achieve the lowest control speed error and the lowest amplitude stator currents with low total harmonic distortion (THD). Also, the optimization was able to achieve the minimum difference between d-q axes currents. The motor was controlled without using a shaft sensor which plays an important role in rotor synchronization. A TMS320F28379D was used as a dual-core microcontroller capable of performing 32-bit floating point operations at 200 MHz operating frequency. The speed estimation algorithm was operated on the other core while the speed control and switching algorithms were operated on the first core. The sampling time for both cores was set to 80us. These two cores were communicated over an internal memory of the microcontroller. Experimental results for both MTPA and EKF-based sensorless algorithms were presented in the paper.

Design of a Research Laboratory Drive System for a Synchronous Reluctance Motor for Vector Control and Performance Analysis

Motor-drive systems have the most significant share in industrial energy consumption, which requires a deep study in every aspect of the field. This paper presents a synchronous reluctance motor (SynRM) drive system based on Plecs RT box 1. The system’s design provides the opportunity for the open-loop and closed-loop control of the motor and a characteristic performance analysis of the motor. This paper focuses on the hardware implementation of a research laboratory setup and the precise vector control of the SynRM in real-time. The application of the digital controller and inverter to drive SynRM is examined. The voltage, current, and speed transducers were employed for monitoring the protective measures and to control the motor in the closed-loop. The design of the signal conditioning and the intermediary cards for isolation and data acquisition are described in detail. An algorithm is proposed to measure the whole system parameters, including motor, inverter, and cables. Thanks to the RT box 1, the principle of real-time simulation of control algorithms is investigated, and the rapid control prototyping of field-oriented control (FOC) of SynRM was implemented. The simulation of the system was carried out in the Plecs platform, and the results are presented. The experimental results of the implemented control algorithms validate the setup’s performance and the control algorithm. Finally, as a study of the motor’s performance, the efficiency map of the motor is drawn in different speed and torque ranges.

Model-Free Predictive Current Control of Synchronous Reluctance Motor Drives for Pump Applications

Climate changes and the lack of running water across vast territories require the massive use of pumping systems, often powered by solar energy sources. In this context, simple drives with high-efficiency motors can be expected to take hold. It is important to emphasise that simplicity does not necessarily lie in the control algorithm itself, but in the absence of complex manual calibration. These characteristics are met by synchronous reluctance motors provided that the calibration of the current loops is made autonomous. The goal of the present research was the development of a current control algorithm for reluctance synchronous motors that does not require an explicit model of the motor, and that self-calibrates in the first moments of operation without the supervision of a human expert. The results, both simulated and experimental, confirm this ability. The proposed algorithm adapts itself to different motor types, without the need for any initial calibration. The proposed technique is fully within the paradigm of smarter electrical drives, which, similarly to today’s smartphones, offer advanced performance by making any technological complexity transparent to the user.