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建立在PID参数整定是一种优化问题这一本质上,尝试使用一种新的群体智能算法(蝙蝠算法)整定PID参数。为了通过仿真实验验证这种PID参数整定方法的可行性,文中选取被控系统的ITAE指标作为蝙蝠算法的目标函数,对于越界粒子采取边界缓冲墙策略。以五种常见过程控制系统模型为被控对象,分别使用Ziegler-Nichols方法,粒子群算法和蝙蝠算法获得PID控制器的参数,并对比了这三种方法所得到PID控制器的闭环系统性能,以及粒子群算法和蝙蝠算法的运行效率。实验结果表明,蝙蝠算法不仅在获得的PID控制器性能上优于Ziegler-Nichols方法和粒子群算法,并在算法运行结果的稳定性和对初始种群分布的依赖性方面优于粒子群算法。
Based on the nature of PID parameter tuning as an optimization problem, we try to use a new swarm intelligence algorithm (bat algorithm) to tune the PID parameters. In order to verify the feasibility of this PID parameter tuning method through simulation experiments, ITAE indicator of the controlled system is selected as the objective function of the bat algorithm and the boundary buffer wall strategy is adopted for the cross-boundary particles. Taking the five common process control system models as the controlled objects, the parameters of PID controller are obtained respectively by Ziegler-Nichols method, particle swarm algorithm and bat algorithm. The performance of the closed-loop system of the PID controller obtained by these three methods is compared. As well as the particle swarm optimization and bat algorithm operating efficiency. Experimental results show that the bat algorithm is superior to the Ziegler-Nichols method and the particle swarm optimization algorithm in the performance of the PID controller and is superior to the PSO in the stability of the algorithm and the dependency on the initial population distribution.