Data-driven robust iterative learning control of linear systems
Data-driven robust iterative learning control of linear systems
复制标题
线性系统的数据驱动鲁棒迭代学习控制
DOI：
10.1016/j.automatica.2024.111646
复制
发表时间：
2024-06
期刊：
影响因子：
6.4
通讯作者：
Zezhou Zhang;Qingze Zou
中科院分区：
文献类型：
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作者：
Zezhou Zhang;Qingze Zou
针对多输入多输出（MIMO）线性系统，提出了一种数据驱动的鲁棒迭代学习控制方法。MIMO线性系统的控制，特别是具有强交叉轴耦合的MIMO线性系统的控制，是具有挑战性的，因为MIMO系统的建模可能是复杂的、耗时的，并且通常需要在鲁棒性和性能之间进行权衡。因此，在当前ILC技术中存在限制。本文的目的是发展一种有效的和易于使用的数据驱动迭代学习控制技术的输出跟踪的MIMO线性系统在随机干扰。该方法避免了复杂的建模过程和鲁棒性与精度之间的权衡，并利用上一次迭代的输入和输出数据构造和更新迭代增益，从而捕捉到系统的动态特性。结果表明，迭代学习控制算法的单调收敛性得到保证，并可以获得一个最佳的增益，以最大限度地提高收敛速度和最小的剩余跟踪误差。所提出的技术是通过三输入三输出压电致动器系统的实验，与自适应多轴逆迭代控制（A-MAIIC）技术相比。实验结果表明，当交叉轴耦合较强时，所提出的方法收敛速度快，收敛性好。
We propose a data-driven robust iterative learning control (ILC) technique to multi-input-multi-output (MIMO) linear systems. Control of MIMO linear systems, particularly with strong cross-axis coupling, is challenging as modeling of a MIMO system can be complicated, time-consuming, and often requires a trade-off between robustness and performance. As such, limitations exist in current ILC techniques. The aim of this paper is to develop an efficient and easy-to-use data-driven ILC technique to output tracking of MIMO linear systems under random disturbance. Through the proposed technique, the complicated modeling process and the robustness-accuracy trade-off are avoided, and the up-to-now system dynamics is captured by constructing and updating the iteration gain using the input and output data in the last iteration. It is shown that monotonic convergence of the ILC algorithm is guaranteed, and an optimal gain can be obtained to maximize the convergence rate and minimize the residual tracking error. The proposed technique is illustrated through experiments on a three-input three-output piezoelectric actuator system, with comparison to the adaptive multi-axis inversion-based iterative control (A-MAIIC) technique. The experimental results show rapid convergence and improved formance of the proposed technique when the cross-axis coupling is strong.