基于改进粒子群优化算法的PID控制器参数优化

PID Parameter Optimization Based on Improved Particle Swarm Optimization Algorithm

PID参数优化是控制领域的热点,其控制效果与比例、积分、微分参数有直接关系.为了改善系统性能,提出用一种改进的粒子群优化算法对PID控制器参数进行优化.该算法引入进化速度因子和聚集度因子对权值进行改进,进而改进了速度更新公式,并引入飞行时间因子以改进位置更新公式.通过3种典型函数证明了该算法的优越性,加快了收敛速度,提高了寻优效率.以典型二阶被控模型为研究对象,将上述算法与其他粒子群算法进行对比,表明改进的粒子群算法得到的PID参数具有更好的控制性能.

PID parameter optimization is a hot topic in control engineering. An improved particle swarm optimization （PSO） algorithm is proposed to optimize PID parameters. PID parameters are selected and the system performance is improved. The factors of evolution speed and aggregation degree of the swarm axe introduced to the algorithm to improve the weight to improve the velocity update formula. A flying time factor is then introduced to improve the location update formula. Advantage of the algorithm is shown by three typical functions, indicating improvement of convergence speed and search efficiency. A typical second order controlled model is selected as an object for research, and results of the algorithm are compared with other PSO algorithms. Experiments show that the optimized PID parameters obtained by using the improved PSO algorithm can achieve good control performance.