Fuzzy control has a distinctive feature in that it can incorporate experts' control rules using linguistic expressions. The authors have presented various types of fuzzy neural networks (FNNs) called Type I-V. The FNNs can automatically identify the fuzzy rules and tune the membership functions of fuzzy controllers by utilizing the learning capability of neural networks. In particular, the Type IV FNN has a simple structure and can express the identified fuzzy rules linguistically. The authors have also proposed a method to describe the behavior of fuzzy control systems based on the fuzzy models. The method can comprehensively express the dynamic behavior of fuzzy control systems and makes easy to know how to modify the fuzzy controllers. This paper studies an acquisition of fuzzy controller for an inverted pendulum using the Type IV FNNs and presents a new method for describing of the behavior of the fuzzy control system. The new method expresses the dynamic ehavior of the fuzzy control system more clearly by incorporating the change of the output of the controlled object. This new rule-to-rule mapping method enables easy modification of the fuzzy control rules. The experimental results illustrate that the method is effective in designing the fuzzy controllers having good performance.
Fuzzy Controllers Design for the Inertia Wheel Inverted Pendulum
