基于遗传算法并联机床电机伺服系统控制参数整定

GA-based control parameter tuning of parallel machine tool motor servo systems

并联机床电机伺服系统控制参数的整定是后续并联机床动力学控制研究的基础。该文作者在提出基于时域特性和动态特性PID参数优化指标的基础上,利用遗传算法分别对伺服系统的速度控制环和位置控制环的多个控制参数进行优化整定,并与传统的Ziegler-Nichols调节法则得到的整定结果进行比较分析。在此基础上,又尝试利用遗传算法对多个控制器的控制参数进行同步优化,并得到了理想的优化结果。最后,将上述优化结果加入到并联机床运动学控制模型中进行实验仿真。仿真结果表明,采用遗传算法对伺服系统多环路控制参数进行优化整定,具有计算过程简单、整定速度快和优化结果好等显著优点。

The control parameter tuning of motor servo systems in parallel machine tools is the basis of the analysis of dynamic control systems in parallel machine tools.Genetic algorithm（GA） was used to optimize the control parameters of the speed control loop and position control loop of the motor servo system separately,after the authors of this paper previously presented the optimized index of PID parameters based on the time-domain performance and dynamic characteristics.The optimized results were compared with those optimized using the traditional Ziegler-Nichols tuning laws.Genetic algorithm was then used to optimize all control parameters of several control loops at the same time with desired results which were used in the kinematics control systems of the parallel machine tools for computer simulation.Simulation results show that the optimization process for optimizing the parameters of several motor servo system control loops based on GA has significant advantages such as simple calculation,higher optimization speeds and satisfactory results.