【摘要】 传统的PID整定方法得到的结果通常不是最优参数,很多学者采用遗传算法和模拟退火方法来解决这个问题.针对遗传算法很容易陷入局部最优值和模拟退火算法收敛速度慢这些缺点,融合了粒子群算法和人工免疫机理的优点,提出了一种基于群体智能的免疫算法,对典型二阶对象进行PID控制器参数优化整定.仿真结果证明了提出的算法有效可行.更多还原

【Abstract】 Conventional PID self-tuning approaches cannot guarantee the resulting parameters to be always optimal.The Genetic algorithms(GA) and Simulated annealing(SA) method have been extensively used in optimizing the PID controllers.However,the GA approach can be trapped into the local optima, and the SA usually converges slowly.In this paper,we propose a novel hybrid optimization algorithm based on the synergy of the particle swarm and artificial immune principles.It is further applied to optimize the PID controllers for achieving the best control performance.Computer simulation results have demonstrated the effectiveness of our swarm immune optimization method.更多还原