Wavelet neural network control for induction motor drive using sliding-mode design technique

Abstract

This paper addresses an adaptive observation system and a wavelet-neural-network (WNN) control system for achieving the favorable decoupling control and high-precision position tracking performance of an induction motor (IM) drive. First, an adaptive observation system with an inverse rotor time-constant observer is derived on the basis of model reference adaptive system theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. The adaptive observation system is implemented using a digital signal processor with a high sampling rate to make it possible to achieve good dynamics. Moreover, a WNN control system is developed via the principle of sliding-mode control to increase the robustness of the indirect field-oriented IM drive with the adaptive observation system for high-performance applications. In the WNN control system, a WNN is utilized to predict the uncertain system dynamics online to relax the requirement of uncertainty bound in the design of a traditional sliding-mode controller. In addition, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results.