多策略改进粒子群优化AGV模糊PID控制

Multi-strategy Improved Particle Swarm Optimization AGV Fuzzy PID Control

为解决AGV在复杂环境下控制精度低下、响应速度慢和鲁棒性差等问题,提出一种基于改进粒子群优化模糊PID控制方法。在粒子群算法(PSO)引入Logistic混沌映射对种群进行初始化,其次对惯性权重和学习因子进行非线性控制更新,提升种群寻优能力和避免陷入局部最优;选取9个测试函数验证改进PSO算法效果。仿真结果表明改进PSO在寻优精度和收敛速度都优于其他3种算法,而且不易陷入局部最优。最后对被控系统进行效果验证,结果表明改进PSO优化模糊PID控制器在常规和外部干扰两种环境下控制性能都优于传统PID和模糊PID,具有高可靠性、高控制精度,满足系统要求。

In order to address the issues of low control accuracy,slow response speed and poor robustness of AGV in complex environments,an improved particle swarm optimization(PSO)based fuzzy PID control method was proposed.The PSO algorithm was enhanced by incorporating Logistic chaos mapping for population initialization,and a non-linear control update was applied to the inertia weight and learning factors,by which the population\s optimization capability was improved and preventing getting stuck in local optima.Nine test functions were selected to validate the effectiveness of the improved PSO algorithm.The simulation results demonstrate that the improved PSO outperforms other three algorithms in terms of optimization accuracy and convergence speed,and it is less prone to getting stuck in local optima.Furthermore,the effectiveness of the improved PSO optimized fuzzy PID controller was validated on the controlled system under normal and external disturbance conditions.The results show that its control performance is superior to the traditional PID and fuzzy PID,with high reliability and high control accuracy,and meets the system requirements.