Supervisory genetic evolution control for induction machine using fuzzy design technique

This study presents a supervisory genetic evolution control (SGEC) system for achieving high precision position tracking performance of an indirect field-oriented induction motor (IM) drive. Based on fuzzy inference and genetic algorithm (GA) methodologies, a newly design 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 stabilized around a predetermined bound region. In addition, the effectiveness of the proposed control scheme is verified by numerical simulation and experimental results, and its advantages are indicated in comparison with a feedback control system.