基于自适应粒子群算法的篦冷机电液位置伺服系统PID参数优化

针对PID控制器的参数选取直接影响到第四代篦冷机刮板在调速过程中的稳定性和调节时间的问题,提出一种新的粒子群算法的PID参数整定方法。新的粒子群算法充分利用了适应函数提供的搜索信息,对传统粒子群算法中的权值进行自适应动态控制,并用飞行时间参数替代了压缩因子,飞行时间参数的变化规律是线性递减,并把它命名为自适应粒子群算法。通过测试函数证明提出的自适应粒子群算法比传统的粒子群算法收敛速度快和寻优效率高,应用改进的方法对篦冷机电液伺服系统进行仿真实验,结果证明自适应粒子群算法的PID参数整定方法比传统的整定方法有更好的控制效果,即调速过程稳定和调节时间快。

PID Parameter Optimization of the Grate Cooling Electro-hydraulic Position Servo System Based on Adaptive Particle Swarm Optimization Algorithm

A new PID parameter tuning method based on the particle swarm optimization (PSO) is proposed in this paper as the parameter selection directly affects the stability and adjusting time in the process of the fourth generation grate cooler scraper speed regulation. The new PSO algorithm which is named self-adaptive PSO in this paper makes full use of the search information provided by the adaptive function and realizes adaptive dynamic control of the weight in traditional PSO algorithm. The compression factor is replaced by the flight time parameter which linearly decreases. It is proved that the SAPSO algorithm has a faster convergence speed and higher search efficiency through the test functions compared with the traditional PSO algorithm. The improved method is used in the simulation experiment of the electro-hydraulic servo system of grate cooler to testify that PID parameter tuning method based on SAPSO can achieve better control effect compared with the traditional tuning method, which means with the new method, he speed regulation process is stable and the adjustment time is faster.