Design and applications of strategy-oriented hybrid intelligent controller for nonlinear dynamical system

Abstract

The design and applications of a strategy-oriented hybrid intelligent controller (SOHIC) are addressed in this paper. First, two hybrid intelligent control methods will be developed, including (1) supervisory oriented genetic algorithm controller (SOGAC): it contains an oriented genetic algorithm controller (OGAC) and a supervisory controller. Compared with enunciated genetic algorithm (GA) control methods, the proposed control method possesses some salient advantages of simple framework, less executing time and good self-organizing properties even for the time-varying system; (2) supervisory grey prediction state feedback linearization controller (SGPSC): a grey uncertainty predictor will be designed to forecast the uncertainty and the predicted data will be fed to the feedback linearization controller on line. Secondly, a simple performance index function will be established according to choose one optimal control method. To verify the effectiveness, the proposed control scheme is applied to control a 3rd-order perturbed nonlinear dynamical system and a decoupling induction motor (IM) drive. The whole control system with the SOHIC possesses the advantages of simple control framework, free from chattering, stable performance and robust to uncertainties. The advantages are indicated in comparison with existing control schemes.