Lyapunov Stability Based Neural Network Controller for Permanent Magnet Linear Synchronous Motor Drive System

• Main Authors: Po-Hung Shen, 沈柏宏
• Other Authors: Faa-Jeng Lin
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/85622740865059424270

碩士 === 國立東華大學 === 電機工程學系 === 91 === The subject of this thesis is to develop a permanent magnet linear synchronous motor (PMLSM) drive control system based on Lyapunov stability theorem. First, the dynamic model of a field-oriented PMLSM drive is derived. Then, a robust fuzzy neural network (RFNN) control system and an adaptive wavelet neural network (AWNN) control system are developed individually for the robust and precise position control of the PMLSM. In the proposed RFNN control system. First, the ideal feedback linearization control law is designed based on the backstepping technique. Then, a FNN controller is designed as the main tracking controller, which is used to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. Moreover, to relax the requirement for the bound of uncertainty, which comprises a minimum approximation error, optimal parameter vectors and higher-order terms in Taylor series, a RFNN control system with adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. In the proposed AWNN control system, a WNN with accurate approximation capability is employed to approximate the unknown dynamics of the PMLSM, and a robust term is proposed to confront the inevitable approximation errors due to finite number of wavelet basis functions and disturbances including the friction force. Furthermore, to relax the requirement for the bound of uncertainty in robust term, which comprises a minimum approximation error, optimal parameter vectors, higher-order terms in Taylor series and friction force, an adaptive bound estimation law is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. All the on-line adaptive learning algorithm of neural networks and the stability of control systems are derived using Lyapunov stability theorem. Finally, the effectiveness of the proposed control schemes is demonstrated by some simulated and experimental results.