摘要
结合粒子群优化算法的进化方程和免疫系统的克隆选择机制,提出一种组合优化算法。该算法有效利用了抗体的历史信息和抗体之间的合作,提高了种群的多样性和算法的收敛速度,并具有全局搜索能力。然后,在此算法的基础上,设计了一PID控制器(PCA-PID),可动态地改变其参数,以适应时变被控对象。仿真程序表明,与单纯的粒子群算法以及克隆选择算法设计的控制器相比,PCA-PID有更好的控制性能。
An advanced algorithm（PCA） was introduced in this paper,which combined the clonal selection mechanism of the immune system with the evolution equation of particle swarm optimization,and had the ability of global searching.The algorithm improved the diversity of antibody population and its convergence speed,by using effectively the past information of the antibodies and their cooperation.Based on PCA,a PID controller（PCA-PID）was designed,which can modified its parameters dynamically to adapt time varying control objects.The simulation results show that PCA-PID has better control performance,compared with the other two controllers by adopting particle swarm optimization and clonal selection algorithm respectively.
出处
《制造技术与机床》
CSCD
北大核心
2010年第11期69-72,共4页
Manufacturing Technology & Machine Tool
基金
博士启动基金(KJ2009B038)
