Intelligent Backstepping Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Position Servo Drive with Recurrent Wavelet Fuzzy Neural Network

Faa-Jeng Lin (), Ming-Shi Huang, Yu-Chen Chien and Shih-Gang Chen
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Faa-Jeng Lin: Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan
Ming-Shi Huang: Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Yu-Chen Chien: Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan
Shih-Gang Chen: Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan

Energies, 2023, vol. 16, issue 14, 1-23

Abstract: An intelligent servo drive system for a permanent magnet-assisted synchronous reluctance motor (PMASynRM) that can adapt to the control requirements considering the motor’s nonlinear and time-varying natures is developed in this study. A recurrent wavelet fuzzy neural network (RWFNN) with intelligent backstepping control is proposed to achieve this. In this study, first, a maximum torque per ampere (MTPA) controlled PMASynRM servo drive is introduced. A lookup table (LUT) is created, which is based on finite element analysis (FEA) results by using ANSYS Maxwell-2D dynamic model to determine the current angle command of the MTPA. Next, a backstepping control (BSC) system is created to accurately follow the desired position in the PMASynRM servo drive system while maintaining robust control characteristics. However, designing an efficient BSC for practical applications becomes challenging due to the lack of prior uncertainty information. To overcome this challenge, this study introduces an RWFNN as an approximation for the BSC, aiming to alleviate the limitations of the traditional BSC approach. An enhanced adaptive compensator is also incorporated into the RWFNN to handle potential approximation errors effectively. In addition, to ensure the stability of the RWFNN, the Lyapunov stability method is employed to develop online learning algorithms for the RWFNN and to guarantee its asymptotic stability. The proposed intelligent backstepping control with recurrent wavelet fuzzy neural network (IBSCRWFNN) demonstrates remarkable effectiveness and robustness in controlling the PMASynRM servo drive, as evidenced by the experimental results.

Keywords: permanent magnet-assisted synchronous reluctance motor (PMASynRM); maximum torque per ampere (MTPA); finite element analysis (FEA); backstepping control (BSC); recurrent wavelet fuzzy neural network (RWFNN); intelligent backstepping control recurrent wavelet fuzzy neural network (IBSCRWFNN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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