Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM

Abstract

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 better tracking precision. Furthermore, the ML model does not require the mathematical model of the speed controller. In this work, several ML models are empirically evaluated on their estimation accuracy for speed tracking, namely ordinary least squares, passive-aggressive regression, random forest, and support vector machine. Finally, the proposed controller is implemented on a real-time hardware-in-the-loop (HIL) simulation platform from PLECS Inc. Comparative simulation and experimental results are presented and discussed. It is shown from the comparative study that the proposed FOSMC based on ML outperformed the traditional sliding mode control (SMC), which is more commonly used in industry in terms of tracking speed and accuracy.

A New Fractional-Order Adaptive Sliding-Mode Approach for Fast Finite-Time Control of Human Knee Joint Orthosis with Unknown Dynamic

“…As the example, the FFOASMC in Equations ( 17)-( 18) is simulated. To do this, the desired trajectory is considered as [46]: (38) where f = 0.16. The initial conditions for the simulation are set as follows:…”

“…A recent method for addressing chattering while maintaining system performance involves employing fractional-order sliding mode controllers (FOSMCs) [36][37][38]. The application of fractional-order SMCs not only mitigates chattering but also enhances closedloop system performance.…”

“…Saif et al recently used a fractional-order sliding mode controller for achieving tracking in quadcopters and had also compared the performance with integerorder SMC. Yu et al proposed a model-free fractional-order SMC along with a nonlinear disturbance observer for electric drive systems in [37], and Zahraoui et al [38] proposed a fractional-order SMC based on machine learning techniques for speed control in permanent magnet motors. There are few papers on the fractional-order TSMC of KJO systems.…”

Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control

“…Therefore, designing controllers using nominal values is bound to be influenced by unknown uncertainties. In this article, unknown uncertainty is summarized as a term in system dynamics [35,36].…”

Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning

“…Mainly, several electromechanical parameters variations, speed, and position tracking control are the most challenging in PMSM [2]. In order to solve these challenges, various advanced control methods have been presented recently [3]. One of the popular methods that have been introduced in the literature is sliding mode control (SMC), which is successfully applied for the PMSM control [4].…”