A new genetic based approach to fuzzy controller design and its application
Ning Xiong, Lothar Litz · 2002
One of the major challenges in the current fuzzy control research is the automatic design of multiple input controllers for complex nonlinear systems. This paper presents a new genetic-based scheme to treat this issue: the so-called premise learning approach. We propose to search in the input domain for suitable rule premises. The rule premises are coded in a general way allowing AND- as well as OR-connections of the linguistic terms, in combination with a certain class of input and output fuzzy sets. The rule structure and the fuzzy sets are optimized by the genetic algorithm at the same time. With this new approach a considerable reduction of the number of necessary rules may be expected. This method is used to design a fuzzy controller to balance an inverted pendulum. Simulations as well as results of a real laboratory plant are shown to demonstrate the effectiveness of the new method.