论文部分内容阅读
研究了磁悬浮系统PID控制器优化设计问题.为寻找最佳的PID控制器参数组合,提出一种新的基于多Agent体系结构的混沌粒子群优化算法(MAS-CPSO).该算法以粒子群算法为基础,将粒子群算法的群搜索特征和Agent的智能搜索特征相结合,提升全局探索能力;同时融合混沌局部搜索算法,增强信息多样性和算法在解空间中的收敛精度.采用改进的误差绝对值时间积分函数(ITAE)作为优化目标,对系统的单位阶跃响应进行仿真.与其它多个算法的动态性能进行比较,结果表明MAS-CPSO算法能快速收敛到最优参数值,并能有效改善磁悬浮系统的性能.
In order to find the optimal combination of PID controller parameters, a new MAS-CPSO (Multi-Agent-based Chaos Particle Swarm Optimization) algorithm is proposed, which is based on Particle Swarm Optimization , The group search feature of particle swarm optimization is combined with the intelligent search feature of Agent to enhance the global exploration ability.At the same time, the chaotic local search algorithm is combined to enhance the information diversity and the convergence accuracy of the algorithm in the solution space.Using the improved error The absolute value time integral function (ITAE) is used as the optimization objective to simulate the unit step response of the system.Compared with the dynamic performance of other algorithms, the results show that the MAS-CPSO algorithm can quickly converge to the optimal parameter value and can Effectively improve the performance of magnetic levitation system.