Wavelet Fuzzy Neural Network With Asymmetric Membership Function Controller for Electric Power Steering System via Improved Differential Evolution

• Published in: IEEE transactions on power electronics Vol. 30; no. 4; pp. 2350 - 2362
• Main Authors: Hung, Ying-Chih, Lin, Faa-Jeng, Hwang, Jonq-Chin, Chang, Jin-Kuan, Ruan, Kai-Chun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Asymmetry; Control systems; Dynamical systems; Dynamics; Electric power; EPS; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Inverters; Neural networks; Propagation; Synchronous motors; Torque; Vehicles; Wavelet; Windings; electric power steering (EPS); Asymmetric membership function (AMF); differential evolution (DE); wavelet fuzzy neural network (WFNN); six-phase permanent magnet synchronous motor (PMSM)
• ISSN: 0885-8993; 1941-0107
• DOI: 10.1109/TPEL.2014.2327693

A wavelet fuzzy neural network using asymmetric membership function (WFNN-AMF) with improved differential evolution (IDE) algorithm is proposed in this study to control a six-phase permanent magnet synchronous motor (PMSM) for an electric power steering (EPS) system. First, the dynamics of a steer-by-wire EPS system and a six-phase PMSM drive system are described in detail. Moreover, the WFNN-AMF controller, which combines the advantages of wavelet decomposition, fuzzy logic system, and asymmetric membership function (AMF), is developed to achieve the required control performance of the EPS system for the improvement of stability of the vehicle and the comfort of the driver. Furthermore, the online learning algorithm of WFNN-AMF is derived using back-propagation method. However, degenerated or diverged responses will be resulted due to the inappropriate selection of small or large learning rates of the WFNN-AMF. Therefore, an IDE algorithm is proposed to online adapt the learning rates of WFNN-AMF. In addition, a 32-bit floating-point digital signal processor, TMS320F28335, is adopted for the implementation of the proposed intelligent controlled EPS system. Finally, the feasibility of the proposed WFNN-AMF controller with IDE for the EPS system is verified through experimental results.