Control Of Permanent Magnet Brushless DC Motor Using Immune Genetic Algorithm Based Fuzzy Logic Controller

As permanent magnet brushless DC motor (BLDCM) is a multi-variable andnon-linear system, conventional controllers can not obtain satisfied control effect.Fuzzy logic control is presented by Zadeh, it can improve the performance ofBLDCM system, but it is difficult to design and set parameters. Genetic algorithm(GA) and other intelligent algorithms are usually solutions. But it is hard to solvethese problems of fuzzy logic controller based on GA as slow convergence, hard tosimultaneously optimize parameters and tend to trap in local optimum. Immunegenetic algorithm (IGA) is inspired by biological immune system, which has manyadvantages.A fuzzy logic controller based on IGA is presented in this paper, which is appliedinto the BLDCM control system. Integrated time and absolute error (ITAE) is used asthe term of fitness function, and the computational efficiency is improved byamending the encoded mode. The whole BLDCM control system is simulated inMATLAB/Simulink. Based on the BLDCM model, the simulation of fuzzy controlsystem is set up. The simulation and experiment results show excellent flexibility andadaptability as well as high precision and good robustness of the proposed strategy.For experiment, the research designs the control system of BLDCM based onDSP TMS320F2812.