改进的粒子群优化控制算法及其仿真研究(英文)

Improved Control Algorithm Based on Particle Swarm Optimization and Its Simulation Research

控制系统的性能是由控制器的控制参数确定的。粒子群优化控制算法中存在的参数选择难题,如基本PSO算法易于陷入早熟收敛现象引起的局部最优解,导致不可能收敛于全局最优解,搜索精度不高以及收敛速度慢.针对以上问题,提出了一种改进的粒子群优化控制算法。讨论了具有遗传思想的粒子群优化算法,研究了改进的PSO控制算法,借助仿真实验对所设计的控制算法作了比较研究。仿真实验结果的响应曲线显示,其动静态特性优于传统方法的响应特性,验证了所提出改进控制算法的合理性与可行性。研究结果表明,所提出的改进PSO控制算法对控制器参数整定更加有效。

The performance of control system is determined by the control parameter of controller. Aimed at the puzzle of parameter selection for particle swarm optimization （PSO） control algorithm that the phenomenon of premature convergence made the basic PSO algorithms have been easy to get in local optimal solution, and resulted in impossible convergence to global extremum, as well as being not so high in search precision and slower in convergence speed, the paper proposed a sort of improved control algorithm based on particle swarm optimization. In the paper, it discussed the particle swarm optimization algorithms with genetic thought （GAPSO）, researched on improved algorithm of PSO, and made the comparative study for proposed control algorithm by means of simulation experiment. The response curve of simulation result demonstrated that it would be better in comparison with conventional method in dynamic and steady performance, and verified the reasonability and feasibility of the improved control algorithm. The research result shows that the improved algorithm of PSO proposed by the paper is more effective for controller parameter tuning.