Abstract
The conventional control theories can be divided into two classes, that is, classical control theory whose objective is to minimize the control error and modern control theories whose objective is to obtain the dynamic control which optimize the integral type performance index. The both class of control theories are different in design concept and structure. On the other hand, control action of most of the fuzzy control is a fuzzified PD or PI control action used in classical control theory. There are many studies on fuzzy dynamic programming. But it is hard to apply fuzzy dynamic programming to control , because it is basically considered as a feedforward control. Since fuzzy control rules can be chosen more freely in order to achieve a higher goal, it should be possible to design fuzzy control system which has both advantages of classical control theory and modern control theory. This paper proposes the new control strategy that the fuzzy optimal control rules are selected when the state variables are apart from the goal and it is switched to ordinary fuzzy PD control rules automatically near the goal in order to solve the problem of stability and off-set. For the optimal control, phase plane is digitized and the optimal output of the controller is obtained by applying fuzzy dynamic programming which has linguistically explained goals and constraints to each state on the phase plane. Quasi-optimal feedback control rules are obtained by describing the optimal output of controller for each state as fuzzy rules whose inputs are state variables. The effectiveness of the system designed with this method is verified by simulations and it was also found that the characteristic of the system is similar to that of fuzzy sliding mode control.
