摘  要：针对一般的粒子群优化（PSO）学习算法中存在的容易陷入局部最优和搜索精度不高的缺点，对改进型PSO算法进行研究。由于惯性权重系数ω对算法是否会陷入局部最优起到关键的作用，因此，通过改变惯性权重ω的选择，对惯性权重系数采取线性减小的方法，引入改进型的PSO算法。采用改进的PSO算法对PID控制器进行参数优化并把得到的最优参数应用于控制系统中进行仿真。仿真实验结果表明：改进型PSO算法不会陷入局部最优，能得到全局最优的PID控制器的参数，并使得控制系统的性能指标达到最优，控制系统具有较好的鲁棒性。
关键词：粒子群优化算法；控制器；参数优化；性能指标；鲁棒性
中图分类号：TP273；TP214；N945.13；TM930.12      文献标志码：A       文章编号：1674-5124（2013）05-0096-03
Research on PID controller based on improved PSO algorithm
ZHANG Yan-hong
（School of Electronic Information & Electric Engineering，Changzhou Institute of Technology，
Changzhou 213002，China）
Abstract: For the shortcomings of easily falling into local optimum and lower search precision in common particle swarm optimization（PSO） algorithm， an improved PSO algorithm was studied. The inertia weight coefficient ω plays a key role on whether PSO algorithm falling into local optimum or not， so it is linearly reduced by the improved PSO algorithm. The parameters of PID controller are also optimized by the improved PSO algorithm and the optimized parameters are used in the control system. The simulation results show that the improved PSO algorithm does not fall into local optimal and the optimal parameters of the PID controller can be obtained， which makes the performance index of the control system optimal with better robustness.
Key words: PSO； controller； parameter optimization； performance index； robustness