改进的PSO算法及其在PID控制器参数整定中的应用

Application of improved PSO for parameter tuning of PID controller

粒子群优化算法(PSO)是一种新兴的随机优化技术,在许多领域得到了广泛应用。为了提高算法的计算精度,加快算法的收敛速度,提出了一种改进的粒子群优化算法,通过引入粒子运动过程中的最差位置信息,由最优个体和最差个体获取信息,有效地提高了算法的搜索能力和收敛速度。在实验研究中,采用改进的粒子群优化算法对PID控制器参数进行整定并用于啤酒发酵过程温度段控制,实验结果表明所提出的算法搜索能力及收敛速度显著提高,应用该方法得到的PID控制器综合性能优于常规方法所得的结果。

A particle swarm optimization （PSO） is a new random optimization technology with many diverse applications. In this paper, an improved algorithm is proposed in order to improve the accuracy of the algorithm and speed up the convergence rate. The developed algorithm gets the information of the particles through taking the worst value and the best value into consideration. In the experimental study, the improved PSO algorithm is adopted to tune the parameters of PID control and applied for temperature control of beer fermentation. The results show that the proposed algorithm is effective and the performance is superior to conventional methods of PID controller.