Publication: Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning
Date
2023
Authors
Zahraoui Y.
Zaihidee F.M.
Kermadi M.
Mekhilef S.
Alhamrouni I.
Seyedmahmoudian M.
Stojcevski A.
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
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 functions in the literature were also compared, including the integral time multiplied square error, integral square error, and integral absolute error. To validate the performance of the RLNNA-FOSMC speed controller, different scenarios with different speeds steps were carried out. The computational results are promising and demonstrate the effectiveness of the proposed controller. Overall, the proposed RLNNA-FOSMC controller for the PMSM speed control system performed better than conventional FOSMC in numerical simulations. � 2023 by the authors.
Description
Keywords
artificial neural network , feedback linearization , fractional-order sliding mode control , nonlinear disturbance observer , PMSM drive , reinforcement learning , Controllers , Errors , Learning algorithms , Learning systems , Neural networks , Parameter estimation , Permanent magnets , Reinforcement learning , Sliding mode control , Speed control , Synchronous motors , Two term control systems , Disturbance observer , Feedback linearisation , Fractional order , Fractional-order sliding mode control , Nonlinear disturbance , Nonlinear disturbance observer , Permanent Magnet Synchronous Motor , Permanent-magnet synchronous motor drives , Reinforcement learnings , Sliding-mode control , Feedback linearization