The Brushless DC Motor Speed Control System Design Based On The Fuzzy Control

The brushless DC motor has been widely used in fields of electromechanicalenergy conversion because of its small size, high power density, simple structure andbetter speed regulation performance, etc.Brushless DC motor has multi-variable, strong coupling, nonlinear and othercharacteristics, so it is difficult to find a suitable PID parameter online and accuratecontrol to achieve the ideal effect. Adaptive control algorithm can identify anddistinguish parameter, estimate motor status relative to linear model. It is unable toobtain satisfactory control effect for nonlinear system. Fuzzy control doesn’t requiregrasp the precise model of the controlled object, a lot of expert control rules are unableto satisfy the control requirements of different objects. Clonal selection algorithm caneffectively search the global optimal solution, and avoid falling into local optimalsolution. In this paper, a variety of control strategies are combined with each other, afuzzy adaptive PID controller based on clonal selection algorithm is designed.This paper describes the structure and operation principle of brushless DC motor,then establishes double closed loop control system by learning its mathematical model.The outer loop module utilize the fuzzy adaptive PID controller which has beenoptimized by multi-objective clonal selection algorithm, the inner module utilize thetraditional PI controller. In addition, this paper presents two kinds of control methods,one is optimizes the fuzzy rules by clonal selection algorithm, the other is control byelitist-guidance mechanism. Both control method can improve the performance of thebrushless DC motor. Compared with the conventional controller, the system responsetime has improved obviously, and be able to reach steady state quickly, it is also hashigher control accuracy relative to the conventional controller. Though the first methodcan obtain the fuzzy rules on the overall optimal, it unable to be satisfied withdecision-making preferences. The second method can make a fast, effective,directional search for Pareto optimal solutions according to decision-making preferences.The simulation model of BLDCM is set up in Matlab2012/Simulink whichincludes motor body’s main circuit module, logic commutation module, speed loopmodule and current PI controller module. The simulation results show that, the systemwhich uses the fuzzy controller based on multi-objective clonal selection algorithmoptimization is able to rise in short time, no overshoot, small steady-state error, strongrobustness and adaptability.