Research On The Design Of Fuzzy Control System And Parameter Optimization With The Algorithm Of PSO

Fuzzy control is an intelligent control solution without model, which is often used in nonlinear processes. However, the lack of high precision and parameter systemic design methods limited the applications of fuzzy control. In this thesis, in order to improve the performances of fuzzy controller, a hybrid structure of fuzzy PID controller is proposed and a systemic parameter optimal method based on the PSO (Particle Swarm Optimization) is introduced. The simulations of some industrial plants and common models validate the effective of above-mentioned method and structure. The main research works are as follows:1. A survey of the origin and the development status of fuzzy control theory is summarized. The status of improving fuzzy control performance is also introduced.2. The general principle of fuzzy control and the structure of conventional fuzzy controller are introduced. The design approach of fuzzy controller is reviewed. Some common fuzzy PID controllers are introduced and analyzed.3. A mixed parameter self-tuning fuzzy PID controller is proposed. The control simulation of the hydraulic turbine generator is built and the results show that the proposed controller has satisfied performances.4. A survey of the Particle Swarm Optimization (PSO) is summarized, including the history and the development of PSO as well as the applications of PSO. Some examples of functions illustrate the effective of PSO.5. PSO is used to solve the systemic parameter design problems of fuzzy controller. The process of parameter optimal design of common controller is first detailed proposed. And then the PSO method is employed in two kinds of simple and useful fuzzy controller. The simulation results with some typical models show that the parameter optimal design method based on the PSO is feasible and effective.