基于改进PSO的矿井提升机控制系统参数优化设计

矿井提升机控制系统PID参数的优化对提高提升机的舒适和安全性能有重要作用。考虑到控制系统对提升机动态响应特性的影响,提出了一种改进的粒子群优化(PSO)算法对提升机控制系统参数进行优化,并在Simulink平台上进行了仿真分析。改进后的PSO算法可以随迭代次数自适应地调节惯性和学习因子,加快收敛,结果更精确。仿真结果表明,使用改进的PSO算法对提升机控制系统的参数进行优化后,系统在动态过渡过程中不会有过冲或振荡,响应更快,抵抗外界扰动因素的能力也更好,从而达到使系统具有高性能的要求。

Optimization Design of Parameters of Mine Hoist Control System Based on Improved PSO

The optimization of PID parameters of the mine hoist control system has an important effect on improving the comfort and safety performance of the hoist.In consideration of the influence of the control system for the dynamic response characteristics of the hoist,improved particle swarm optimization(PSO)algorithm to optimize the parameters of the hoist control system was proposed,simulation analysis was performed in Simulink platform.The improved PSO algorithm can adjust the inertia factor and learning factor adaptively with the number of iterations,speed up the convergence speed,and the result is more accurate.The simulation results show that after using the improved PSO algorithm to optimize the hoist control system parameters,the system will not have overshoot or oscillation during the dynamic transition process,the response speed is faster,and the ability to resist external interference is also better.The system meets requirement of high performance.