Modified Particle Swarm Optimization With Application Of Fuzzy Controller Design

Fuzzy Control(FLC) technology has the simple controller design,the characteristics of robustness,especially for control of nonlinear systems.Since the 80s of last century,in theoretical and practical aspects of the project are widely used.The greatest advantage of FLC is in the fuzzy set theory,based on the language to describe those difficult analytical mathematical description of the system characteristics and control methods,so that people can be directly applied to control the process of intelligence.However,it is a kind of fuzzy controller based on the expertise of non-linear controller,how to accurately determine the fuzzy set of membership functions and fuzzy control rules,Lack of effective design.The existence of the traditional fuzzy controller for the inadequacies of this paper,based on particle swarm optimization algorithm(PSO) of the FLC design.First of all,the basic PSO algorithm for local optimization easily into the defect,through the analysis of inertia weight and learning factors on algorithm performance,this paper presents an improved algorithm: Based on the inertia weight and learning time-varying sub-factor PSO algorithm(WFPSO). Benckmark standard test function experiments show that the improved PSO algorithm to improve the global search ability of particles and speed up the convergence rate of particles. Secondly,this improve the PSO algorithm is applied to the design of fuzzy controller,on-line fuzzy controller automatically adjusts the quantization factor,ke,kec and scale factor ku, system better fast-no overshoot of the dynamic performance.Brushless DC motor is a multi-variable and non-linear control systems,the traditional control is difficult to meet the precise control of it.Therefore,the improveed particle swarm optimization algorithm is applied to the fuzzy controller of brushless DC motor control,the motor starting torque,adjustable speed fast with the traditional fuzzy control system for comparative study.The simulation results show that the improved fuzzy controller has a small tart torque ripple,has a litter non-overload,has the fast rise and a short transition time.