Rule Mapping Fuzzy Controller Design.

• Main Authors: Cheng-Shun Fan, 范振順
• Other Authors: Ching-Chang Wong
• Format: Others
• Language: zh-TW
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/76853725128028467604

碩士 === 淡江大學 === 資訊工程研究所 === 83 === It is difficult for a fuzzy logic controller designer to determine the universe of discourse of the input and output linguistic variable, the shape of the membership function and the fuzzy control rule of the controller. In general,these parameters of the controller is based on the expert knowledges and the operator''s experiences. It needs a very time-consuming trial-and- error procedure to finely tune these parameters. In this thesis, we propose a rule mapping fuzzy controller. It is constructed by the method of rule mapping and Genetic Algorithms under the innocences of the controlled process. The system response can be described by the transient and steady state characteristics. Various system needs different performance. To meet the different specifications of the controlled system, we propose a weighted type of the fitness function in Genetic Algorithms for various different purposes so that a satisfactory performance (fast rise time, small maximum overshoot and small integral of absolute error) in the step response can be obtain. Simulation results of the inverted pendulum system demonstrate the efficiency of the proposed control scheme. On the other hand, the most effective way to improve the performance of a fuzzy controller is to optimize its fuzzy control rule. If the control rules can change according to the states of system''s response,then the system can obtain a comparatively better performance.Since the control rules of the original rule mapping fuzzy controller are decided by a regulating factor, we employ a Rule Self-Regulating Mechanism to change the regulating factor for different states of system responses. That is fuzzy control rules can be regulated on line. The simulation shows that the performance of self-regulating fuzzy controller can be improved further than that of a non-self -regulating fuzzy controller.