基于改进粒子群算法的主汽温系统PID参数优化

应用改进的粒子群优化算法优化PID参数。采用动态变量区间以逐步缩小搜索区间，加快粒子群寻优速度，并且针对粒子群算法可能出现的停滞现象，引入了重新启动策略，改善了算法摆脱局部极点的能力。通过对具有严重参数不确定性、多扰动以及大迟延的电厂主汽温被控对象的仿真研究，结果表明：改进的粒子群算法寻优速度快，计算量小，对PID参数优化是非常有效的，使得主汽温控制系统取得了很好的控制品质，系统鲁棒性比较强。

PID controller parameters optimization for the main steam temperature system based on improved particle swarm optimization

The improved particle swarm optimization (PSO) is used to optimize the PID controller parameters. The dynamic variable interval is used to reduce the searching zone and to speed up particle swarm optimization. According to the delay phenomena that may appear in the particle swarm algorithm, the reboot strategy is introduced. It improves the ability of the algorithm to break away form the local zenith. The controlled objects of main steam temperature in power plant are studied with simulation. The simulation results show mat the improved PSO is very effective.