基于IPSO的数控机床进给伺服系统PID参数优化

摘要

The high precision feed control of CNC machine tools generally adopts permanent magnet syn-chronous motor to drag the AC servo system. Optimizing the PID parameters of the feed axis servo system has an important role in improving the rapid response, followability and anti-jamming of the feed axis.Ai-ming at the high precision requirement of CNC machine tools, this paper proposes an improved particle swarm optimization algorithm (IPSO), which is optimized by using the three scale factor parameters Kp, Ki,Kd of fuzzy controller (PID) of CNC servo machine feed servo system,and in SimuLink environment In the simulation experiment,and the crowd search algorithm, the basic particle swarm algorithm, genetic algorithm for comparison. The simulation results show that the dynamic performance of CNC servo system with PID parameters is improved greatly by IPSO algorithm, which has the advantages of small overshoot, robustness and high steady-state precision.%数控机床的高精度进给控制一般都采用永磁同步电机拖动的交流伺服系统,优化进给轴伺服系统PID参数对提高进给轴快速响应性、跟随精度和抗干扰性等均具有重要作用.针对数控机床高精度要求,文章提出了一种改进粒子群算法(Improved Particle Swarm Optimization,IPSO),利用其对数控机床进给伺服系统模糊控制器(PID)的三个比例因子参数Kp、Ki、Kd进行优化,并在SimuLink环境中进行仿真实验,同时与人群搜索算法(SOA)、基本粒子群算法(PSO)、遗传算法(GA)进行对比.仿真结果表明,利用IPSO算法进行PID参数优化的数控机床伺服系统动态性能得到了很大改善,具有低超调量、鲁棒性强和高稳态精度等优点.