Multi-strategy Improved Particle Swarm Optimization AGV Fuzzy PID Control
In order to address the issues of low control accuracy,slow response speed and poor robustness of AGV in complex envi-ronments,an improved particle swarm optimization(PSO)based fuzzy PID control method was proposed.The PSO algorithm was en-hanced 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.
万方数据
维普
