Supervisory genetic evolution control for indirect field-oriented induction motor drive

Abstract

A supervisory genetic evolution control (SGEC) system for achieving high-precision position tracking performance of an indirect field-oriented induction motor (IM) drive is addressed. Based on fuzzy inference and genetic algorithm (GA) methodologies, a newly designed GA control law is developed first for dominating the main control task. However, the stability of the GA control cannot be ensured when huge unpredictable uncertainties occur in practical applications. Thus, a supervisory control is designed within the GA control so that the states of the control system are stabilised around a predetermined bound region. The salient features of this study are that the spirit of a fuzzy inference mechanism is utilised in the design of GA control with a self-organising property, and that the stability of the SGEC system can be guaranteed with the aid of a supervisory control. In addition, the effectiveness of the proposed control scheme is verified by numerical and experimental results, and its advantages are indicated in comparison with a feedback control system.