PSO算法及在二自由度PID调节器优化设计中的应用

Particle Swarm Optimization Algorithm and its Application to Optimal Designing of Two-degree-of-freedom PID Regulator

微粒群算法是一种群体智能算法,它是通过模拟以鸟类、昆虫等微粒的自然界的群体行为,构造的一种随机寻优的进化算法。基于灵敏度函数,采用PSO算法对二自由度PID参数进行自适应调整,提出了一种新二自由度PID调节器的设计方法,可以有效抑制参数变化对系统的影响,使系统同时获得良好的目标值跟踪特性、干扰抑制特性和鲁棒性,以工业过程中常见的对象模型进行了仿真实验,仿真结果和改进的遗传算法进行比较,表明了这一方法的有效性,证明了PSO算法的有效性。

Particle Swarm Optimization （PSO） is a kind of Swarm Intelligence Algorithm and it is inspired by the behavior of swarms of insects in nature. Based on sensitivity function, the parameters of two degree of freedom PID regulator are adaptively adjustable using Particle Swarm Optimization（PSO） algorithm, A kind of new design method for two degree of freedom （2DOF） PID regulator was presented. Very good dynamic response performance of both the command tracking and disturbance rejection characteristics and robustness can be achieved simultaneously, the effectiveness of the proposed optimization algorithm is tested in the common industrial models, comparisons of simulation results with the improved GA were given to demonstrate the effectiveness of the proposed method.