In this article, to guarantee the good tracking performance of the precision motion system for various tracking tasks, an online iterative learning compensation method is proposed for closed-loop motion control systems. The prediction model is based on the closed-loop model of the linear second-order system with a proportional-integral-derivative controller, and an estimation term is added to deal with the influence of slow-varying uncertain disturbances. On the basis of the accurate state prediction, the dynamical feedforward compensation can be obtained, which suppresses the tracking error caused by the dynamical lag. Furthermore, in order to simultaneously compensate the errors caused by nonlinear factors such as uncertain disturbances and to guarantee the smoothness of the compensated trajectory, the optimal compensation gain is determined through online iterative calculation. The online iterative approach is similar to iterative learning control, but does not require several offline iterations of a repeating trajectory. Comparative experiments are carried out on an industrial motion stage. Various experimental results consistently demonstrate that the proposed compensation scheme can achieve the tracking accuracy comparable to iterative learning, while maintaining the robustness to trajectory changes and uncertain disturbances without reoffline iteration.

中文翻译：

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基于模型预测的轨迹跟踪控制系统在线迭代学习补偿方法


本文为了保证精密运动系统在各种跟踪任务中具有良好的跟踪性能，提出了一种闭环运动控制系统的在线迭代学习补偿方法。该预测模型基于比例积分微分控制器的线性二阶系统闭环模型，并添加估计项来处理慢变化的不确定扰动的影响。在准确的状态预测的基础上，可以获得动态前馈补偿，抑制动态滞后引起的跟踪误差。此外，为了同时补偿不确定扰动等非线性因素引起的误差，保证补偿轨迹的平滑性，通过在线迭代计算确定最优补偿增益。在线迭代方法类似于迭代学习控制，但不需要重复轨迹的多次离线迭代。在工业运动台上进行了对比实验。各种实验结果一致表明，所提出的补偿方案可以实现与迭代学习相当的跟踪精度，同时保持对轨迹变化和不确定干扰的鲁棒性，而无需重新离线迭代。