Filtered-version iterative learning linear servo system with forgetting factor
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摘要: 针对永磁直线同步电机伺服系统,提出开闭环迭代学习控制器,实现期望直线位置的跟踪控制.分析了永磁直线同步电机的2-D模型及迭代学习直线伺服系统的收敛性.通过减小系统输入误差协方差矩阵迹的方式得到优化的遗忘因子,来修正控制输入的迭代学习律,同时采用零相位FIR数字滤波器对前馈学习控制器中的误差信号进行滤波处理.实验结果表明,带有遗忘因子的滤波器型迭代学习控制器能够保证直线伺服系统在不断的迭代学习中提高性能,有效抑制端部推力波动,系统具有很好的学习收敛速度、动态响应及控制精度.Abstract: An open-closed loop iterative learning controller was proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo system to track expectation linear position. The two-dimensional model of PMLSM and the convergence of the iterative learning linear servo system were analyzed in detail. The forgetting factor was optimized by reducing the trace of the input error covariance matrix. This factor is able to modify the iterative learning law of control input. The error signal of the feed-forward learning controller was filtered by a zero-phase FIR digital filter. Experiment results demonstrate that the filtered-version iterative learning controller with forgetting factor can surely improve the performance of the servo system in iterative learning process and effectively suppress the ripple of end force. The system has good learning convergence speed, dynamic response and control precision.
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Keywords:
- linear servo /
- iterative learning /
- forgetting factor /
- filter /
- convergence
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