Supervisory enhanced genetic algorithm control for indirect field-oriented induction motor drive

A supervisory enhanced genetic algorithm control (SEGAC) system is proposed for an indirect field-oriented induction motor (IM) drive to track periodic commands. The proposed control scheme comprises an enhanced genetic algorithm control (EGAC) and a supervisory control. In the EGAC design, the spirit of gradient descent training is embedded in genetic algorithm (GA) to construct the major controller for searching optimum control effort under the possible occurrence of uncertainties. To stabilize the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is designed within the EGAC. The effectiveness of the proposed control strategy is verified by numerical simulation and experimental results, and its advantages are indicated in comparison with a conventional supervisory genetic algorithm control (SGAC) system in the previous works.