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磁悬浮系统是一个典型的非线性复杂系统,具有不确定性和开环不稳定性为了获得更好的磁悬浮小球系统控制性能,本文对系统的控制算法进行了深入研究。PID是经典的控制器,其性能主要受三个参数(K_p,K_ i,K_d)影响。不同于传统的试凑法获得PID参数,本文在这里运用粒子群算法来优化设计PID控制器的三个参数。粒子群算法是一种群优化算法,具有搜索速度快、效率高,适合于实值型处理等优点,此外本文还选取了另外一种常见的优化算法差分进化算法来跟粒子群算法进行比较。本文首先用Matlab对粒子群算法进行编程;其次通过Simulink对试凑法、粒子群优化法、差分进化优化法建模并进行仿真实验;最后比较所生成的阶跃响应曲线和对应的性能指标,得出粒子群算法作用于PID控制器可获得更好的动态性能和稳定性能的结论。
Maglev system is a typical non-linear complex system with uncertainty and open-loop instability. In order to obtain better control performance of maglev system, the paper studies the control algorithm of the system in-depth. PID is a classic controller whose performance is mainly affected by three parameters (K_p, K_i, K_d). Different from the traditional trial and error method to obtain PID parameters, this article uses particle swarm optimization algorithm to optimize the design of the three parameters of the PID controller. Particle swarm optimization algorithm is a swarm optimization algorithm with the advantages of fast search speed, high efficiency, suitable for real-valued processing and so on. In addition, this paper also selects another common differential algorithm evolutionary algorithm to compare with particle swarm optimization algorithm. In this paper, the particle swarm optimization (PSO) algorithm is programmed by Matlab. Secondly, the trial and error method, particle swarm optimization and differential evolution optimization are used to simulate the particle swarm optimization algorithm. Simultaneously, the step response curve and the corresponding performance index are compared. It is concluded that the particle swarm optimization algorithm can get better dynamic performance and stability performance in PID controller.