Adaptive wavelet neural network control for linear synchronous motor servo drive

Abstract

We propose an adaptive wavelet neural network (AWNN) control system to control the position of the mover of a permanent-magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories. The AWNN control system, uses a wavelet neural network (WNN) with accurate approximation capability to represent the unknown dynamics of the PMLSM. It also uses a robust term to confront the inevitable approximation errors due to the finite number of wavelet basis functions and to disturbances, including the friction force. An adaptive learning algorithm that learns the parameters of weight, dilation, and translation of the WNN on line is based on the Lyapunov stability theorem. To relax the requirement for the bound of uncertainty in the 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 used; in the estimation, a simple adaptive algorithm estimates the bound of uncertainty. Our simulated and experimental results for periodic reference trajectories show that the dynamic behavior of the proposed control system is robust with regard to uncertainties. An adaptive wavelet neural network (AWNN) control system is proposed to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive system to track periodic reference trajectories in this study. 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. The adaptive learning algorithm that can learn the parameters of weight, dilation and translation of the WNN on line is derived using Lyapunov stability theorem. Moreover, 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. Furthermore, the simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.