Speed Control Based Particle Swarm Optimizing Clonal Algorithm for AC Induction Motor

Wang Qiang, Chen Jun, Jianxiu Xiao, Sun Jian
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引用次数: 2

Abstract

An intelligent optimizing algorithm, particle swarm optimizing clonal algorithm (PSOCA) was introduced in this paper, which combined the clonal selection mechanism of the immune system with the evolution equation of particle swarm optimization. It had the ability of global searching. The PSOCA improves the diversity of antibody population and its convergence speed, by using effectively the past information of the antibodies and their cooperation. Based on the PSOCA, a PID controller???PCA-PID???is designed, which can modify its parameters dynamically to adapt time varying control objects. PCA-PID controller is exerted to control AC speed system, then its control performance is compared with that of the other two controllers designed by PSO and clonal selection algorithm respectively. The simulation results show that PCA-PID has better control performance, compared with the other two controllers.
基于粒子群优化克隆算法的交流感应电机速度控制
将免疫系统的克隆选择机制与粒子群优化的进化方程相结合,提出了一种智能优化算法——粒子群优化克隆算法(PSOCA)。它具有全局搜索的能力。PSOCA有效地利用抗体的历史信息和抗体之间的合作关系,提高了抗体种群的多样性和收敛速度。在PSOCA的基础上,设计了PID控制器PCA-PID。,可动态修改其参数以适应时变控制对象。采用PCA-PID控制器对交流调速系统进行控制,并与采用粒子群算法和克隆选择算法设计的另外两种控制器的控制性能进行了比较。仿真结果表明,与其他两种控制器相比,PCA-PID具有更好的控制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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