基于量子粒子群算法的PID参数自整定方法

PID控制在工业生产中应用非常广泛.以直流电机模型为被控对象,提出了基于量子粒子群算法的PID参数自动整定方法.应用经典的Ziegler-Nichols方法整定PID参数,被控对象性超调大往往难以满足要求.粒子群算法是通过模拟鸟群觅食过程中的迁徙和群聚行为而提出的一种基于群体智能的全局随机搜索算法.将量子粒子群算法用于优化PID参数,并与Z-N法整定的PID控制器性能进行对比.仿真结果发现,与Z-N法相比,基于粒子群算法优化的PID控制器,系统超调明显减小.除QPSO-PID(ITSE)对应的系统具有较长调节时间外,虽然应用不同优化目标优化后的PID参数不同,控制对象的响应性能却非常相似.

Auto-Tuning of Optimum PID Controller Parameters Based on Quantum-Behaved Particle Swarm Optimization

PID controllers have been widely used for speed and position control of various applications.Quantum-behaved particle swarm optimization(QPSO) is proposed to search for the optimum parameters of PID controller.The model of a DC motor is used as a plan in this research.The conventional gain tuning of PID controller such as Ziegler-Nichols method usually produces a big overshoot,which is not preferable performance.Particle swarm optimization is a heuristic global optimization method,arising from the research of bird and fish flock movement behavior.Comparison between controllers tuned by QPSO and Z-N methods is carried out.Results demonstrate that designed PID controller using QPSO has less overshoot and smaller settling time.Furthermore,the QPSO-based PID controllers for different performance index have similar performances,except that is optimized by ITSE where long settling time is seen.