新型的PID控制器参数整定方法

Novel PID controller parameters tuning method

粒子群优化算法在进化中随种群多样性降低易出现早熟收敛等问题。针对这一问题,结合全局-局部最优模型,提出了一种改进的粒子群优化算法,称为全局-局部参数最优的粒子群优化算法。算法利用全局-局部最优惯性权重及全局-局部最优加速度常数,算法的速度更新方程被简化,性能得到改善。利用一组bench mark问题对该算法进行测试,仿真结果表明了算法的有效性和高效性。将该算法应用到对传统PID控制器的参数优化当中,仿真结果表明方法可以获得满意的控制效果,各项控制性能指标优于传统方法整定得到的PID控制器。

There exists the disadvantages such as prematurity in particle swarm optimization because of the decrease of swarm diversity.In order to solve this problem,a new and improved version of particle swarm optimization algorithm is proposed combining the global best and local best model,termed GLBest-PSO algorithm.This algorithm incorporates global-local best inertia weight with global-local best acceleration coefficient.The velocity equation of the GLBest-PSO algorithm is simplified and the performance of the algorithm is improved.The ability of the GLBest-PSO algorithm is tested with a set of bench mark problems and the results of the simulation show the validity and better optimization performance.The algorithm is proposed to design the parameter optimization of PID controller.The simulation results show that the optimal PID controller based on the proposed method has a satisfying performance and is superior to the conventional PID controller based on the conventional tuning methods.