Optimization and Simulation of a Fuzzy Controller Based on Stochastic Bilinear Systems

As we well know that through the ordinary analysis and simulation we can get membership function and output varying curve after the genetic optimization, the system generate overshoot when close to stable, and the corresponding time is long. The introduction of quantization factor realized variable transformation between the basic and the corresponding fuzzy-sets theory domain, which make the fuzzy control algorithm can calculate the input variables, and take the calculated output act on the controlled object. This text is started from affecting characteristics of the system by quantization factors, put forward a regulator with intelligent quantization factor of fuzzy control algorithm, the regulator regulate quantization factor of error changing rate on line according to the size of absolute value of error, thus it make the system has a better dynamic and static performance. Using this controller to simulate on the bilinear model, and the results prove its effectiveness.