多轴精密运动台前馈控制器参数整定方法

Parameter Tuning Method for Multi-axis Precision Motion Platform Feedforward Controller

为了优化精密运动台在高速、高加速和多自由度耦合条件下的控制性能,提出了一种基于迭代学习和粒子群算法的前馈控制方法,旨在提高带有平衡质量电机的粗微动精密运动台的控制精度和稳定性。构建了运动台的优化模型,考虑了运动台动力学特性及其相互影响。通过引入迭代学习控制算法,控制系统能够不断调整控制输入,以最小化实际输出与期望输出之间的误差。利用学习获得的反馈误差和历史数据,控制系统能够适应运动台的动态变化和非线性,提高了控制系统精度。实验结果表明,基于迭代学习和粒子群算法的控制方法显著改善了运动台的控制精度和稳定性。该方法的应用使运动台能够实现更精确和稳定的控制,进一步提高了半导体设备加工质量和效率,具有重要的实际意义。

In order to optimize the control performance of precision motion stages under high-speed,high-acceleration,and multi-degree-of-freedom coupling conditions,a feedforward control method based on iterative learning and particle swarm optimization is proposed to improve the control accuracy and stability of coarse-fine precision motion stages with balanced mass motors.An optimized model of the motion stage is established,considering the dynamic characteristics and mutual influences of the motion stage.By introducing the iterative learning control algorithm,the control system can continuously adjust the control input to minimize the error between the actual output and the expected output.Using the learned feedback error and historical data,the control system can adapt to the dynamic changes and nonlinearity of the motion stage,improving the accuracy of the control system The experimental results show that the control method based on iterative learning and particle swarm optimization significantly improves the control accuracy and stability of the motion stage.The application of this method enables the motion stage to achieve more accurate and stable control,further improving the processing quality and efficiency of semiconductor equipment,which has important practical significance.