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In pursuit of improving energy efficiency in the industry, this paper proposes two model-based loss-minimization techniques for synchronous reluctance motors (SynRM): the model-based loss-minimization robust method (MLR) and the simplified model-based loss-minimization robust method (SMLR). Both techniques belong to the broader category of model-based methods, which are known for their fast dynamic convergence, low torque ripple, and reduced computational load compared to search-based and hybrid methods. Results reveal that both MLR and SMLR exhibit appropriate performance against motor parameter variations, particularly in the components of stator inductance ( $L_{dq}$ ), while maintaining high efficiency and minimal power loss. The study paves the way for the integration of these techniques into control strategies that use active flux to further enhance the efficiency of SynRM in industrial applications. The paper presents a comprehensive and thorough sensitivity analysis, along with detailed experimental results, that effectively verify the proper and consistent performance of the proposed methods across various operating points.

This study introduces a novel approach to enhance state and unknown input estimation for the synchronous reluctance motor, utilizing the Takagi-Sugeno fuzzy representation. A proportional multi-integral observer structure is introduced, capable of capturing a wider range of unknown input dynamics compared to the previous proportional integral observer utilized in prior research. Stability conditions, obtained using the quadratic Lyapunov function, are formulated as linear matrix inequalities to guarantee the asymptotic convergence of the estimation error. To evaluate the effectiveness of the proposed observer, different cases were considered, including scenarios with slow and fast variations of the unknown input. Through hardware-in-the-loop validation, the results clearly demonstrate the superiority of the proposed method over the earlier approach. This advancement signifies a significant improvement in the estimation accuracy and reliability of the Synchronous Reluctance Motor.

Air-conditioners are now widely being used in offices and domestic homes due to the rising global temperatures. Inverter-driven air-conditioners offer superior cooling performance, enhanced control, and a more compact design than non-inverter-driven air-conditioners. Further, inverter-driven air conditioners are more efficient and also cost-effective within a pay-back period. Nowadays, efficient motor drives that meet international efficiency (IE) standards are the need of the hour. Permanent magnet-assisted reluctance motor (PMa-SyRM) drives are one such feasible solution for air conditioner applications, where very less magnet volume is used thereby improving the efficiency whilst minimizing the overall cost. This paper presents a variable-speed rare-earth-free ferrite Al-Ni-Co hybrid PMa-SyRM drive for an air conditioner application. Hall-sensors are used for detection of the rotor position and a simple current advance angle based control is implemented to extract both the magnetic and reluctance torque components from the hybrid PMa-SyRM. Later, simulation studies are carried-out and the cost and the efficacy of the proposed control is compared with that of the conventional maximum torque per ampere (MTPA) control. Finally, a prototype drive is fabricated and experimental validation under various load conditions is carried-out and the results are presented.

In this paper, a modulated dual-voltage-vector model-free predictive current control with online duty cycle calculation is proposed and applied to drive a synchronous reluctance motor. The dual-voltage-vector modulation scheme reduces the current ripples and errors in the single-voltage-vector method. 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 stator current, reducing the maximum average current error by 34.8% compared to the conventional single-voltage-vector scheme. Finally, the correctness and feasibility of the modulated model-free predictive current controller with the online duty cycle calculation proposed in this article are verified by the experimental results using Texas Instruments microcontroller TMS320F28379D.

In order to achieve improved performance and efficiency of domestic and industrial fan/pump applications using low-cost solutions, the synchronous reluctance motors (SynRMs) remains a better choice than induction motors (IMs) which are still widely used for such applications despite its low efficiency during low speed operation. However, due to the lack of self-start capability the SynRMs need external drive and speed control algorithm to properly operate, which increases the production cost. For the purpose of providing low cost solutions for the SynRMs speed control, this paper presents a stable, reliable and robust scalar control for SynRMs. This control strategy implements the maximum torque per ampere loop for high efficiency operation and online parameter estimation for robust performance. The proposed solution presented in this paper can be implemented using a low cost MCU in PWM inverter with lower carrier frequency, thereby making the low cost SynRM drive for fan/pump application feasible and practical.

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 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.

This paper presents a modulated model-free predictive current control for four-switch three-phase inverter-fed synchronous reluctance motor drive systems to improve performance against existing methods. The study focuses on six switching modes modulated with variable duty ratios that are optimized and computed in real-time. The stator currents corresponding to the six modulated switching modes are predicted without relying on the motor’s mathematical model nor parameters thanks to its model-free nature. To enhance the performance of the controller, adaptive current detection technique and current difference modification are employed, yielding good adaptation to the duty ratio modulations. Implementation of the proposed method is realized via a TMS320F28379D microcontroller on a testbed to assess its effectiveness. Finally, comparisons between the proposed scheme and a non-adaptive predecessor under various experimental settings are made to demonstrate its salient performance.

This paper investigates the performance of 3-phase synchronous reluctance machines (SynRMs) when upgrading their stator to 5-phase, keeping the same rotor. The design of the 5-phase stator has been optimized to select the optimal dimensions of the slots and teeth. Moreover, a combined star-pentagon winding is employed to further improve the machine performance. Different winding configurations have been studied and compared using 2D Ansys Maxwell transient simulations. It is observed that at optimal current angle and rated current, the average torque is increased by 17.41% when changing the 3-phase stator with the proposed 5-phase one. In addition, the efficiency of the 5-phase SynRM is increased by about 0.8% compared to 3-phase SynRM. At 3 times the rated speed, the torque and efficiency are significantly increased by around 33% and 3.5% respectively. Moreover, the 5-phase SynRM shows a superior performance in the faulty case with one phase opened. It works at 98.84% of the rated torque of the healthy 3-phase machine, whereas the 3-phase machine works at only 43.35% with huge torque ripple (228%). Finally, an experimental validation using the reference 3-phase machine has been done.