Design of GA-Based Control for Electrical Servo Drive

Abstract

An alternative control scheme including a directional genetic algorithm controller (DGAC) and a supervisory controller is developed to control the position of an electrical servo drive in this study. In the DGAC design, the spirit of gradient descent training is embedded in genetic algorithm (GA) to construct a main controller to search optimum control effort under possible occurrence of uncertainties. In order to ensure the system states around a defined bound region, a supervisory controller, which is derived in the sense of Lyapunov stability theorem, is added to adjust the control effort. Compared with enunciated GA control methods, the proposed control scheme possesses some salient advantages of simple framework, fewer executing time and good self-organizing properties even for nonlinear dynamical system. The effectiveness is demonstrated by simulation results, and its advantages are indicated in comparison with other GA control schemes for a field-oriented control induction motor drive.