This paper proposed a fractional order PID sliding mode control (FOSMC-PID) for speed regulation of permanent magnet synchronous motor (PMSM). Fractional calculus has been incorporated in sliding mode controller (SMC) design to enhance chattering suppression ability. However, the design of fractional sliding surface is crucial to ensure that speed tracking accuracy is not jeopardized. The proposed controller is designed with a fractional order PID sliding surface, which balances the characteristics of sliding surface with PI or PD structure in terms of robustness and dynamic performance of the controller. By simulation, speed tracking is proven to be faster and more robust with the proposed controller compared to SMC with integer order. Both integration and derivative terms in the surface design outperform FOSMC-PI and FOSMC-PD in terms of disturbance rejection and chattering. Experimental validation proves the advantage of the proposed controller in terms of robustness. ..

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Fractional order sliding mode control has been applied for speed control of PMSM. However, in many previous works, the effects of the controller's parameters have not been studied. This paper investigates the effects of fractional order on performance of FOSMC speed control of PMSM. In this work, fractional order, ? and ? of FOSMS-PID were varied, and their performances were compared. The simulation and experimental results show that variation of order of fractional order integration, ? and order of fractional order differentiation, ? can affect the performance of the FOSMC-PID controller. Selection of ? and ? values determines balancing strategies between integral and differentiation portion of the controller. Proper value selection and combination of these variables can further contribute to obtain optimum speed tracking, disturbance rejection and chattering reduction abilities. 2023, Penerbit Akademia Baru. All rights reserved...

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An improved fractional-order sliding mode control (FOSMC) for PMSM is presented in this study to set the unavoidable parameters and to improve permanent magnet synchronous motors (PMSMs) drive performance, such as current and speed tracking accuracy. To determine the optimal parameters of the FOSMC for control speed in a PMSM drive, a neural network algorithm with reinforcement learning (RLNNA) is proposed. The FOSMC parameters are set by the ANN algorithm and then adapted through reinforcement learning to enhance the results. The proposed controller using RLNNA based on fractional-order sliding mode control (RLNNA-FOSMC) can drive the motor speed to achieve the referred value in a finite period of time, leading to faster convergence and improved tracking accuracy. For a fair comparison and evaluation, the proposed RLNNA-FOSMC is compared with conventional FOSMC by applying the integral of time multiplied absolute error as an objective function. The most commonly used objective functio.....

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Tracking the speed and current in permanent magnet synchronous motors (PMSMs) for industrial applications is challenging due to various external and internal disturbances such as parameter variations, unmodelled dynamics, and external load disturbances. Inaccurate tracking of speed and current results in severe system deterioration and overheating. Therefore, the design of the controller for a PMSM is essential to ensure the system can operate efficiently under conditions of parametric uncertainties and significant variations. The present work proposes a PMSM speed controller using machine learning (ML) techniques for quick response and insensitivity to parameter changes and disturbances. The proposed ML controller is designed by learning fractional-order sliding mode control (FOSMC) controller behavior. The primary purpose of using ML in FOSMC is to avoid the self-tuning of the parameters and ensure the speed reaches the reference value in finite time with faster convergence and bette.....

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