Robust Data-Driven Iterative Learning Control for Linear-Time-Invariant and Hammerstein–Wiener Systems
Robust Data-Driven Iterative Learning Control for Linear-Time-Invariant and Hammerstein–Wiener Systems
复制标题
线性时不变和 Hammerstein-Wiener 系统的鲁棒数据驱动迭代学习控制
DOI：
10.1109/tcyb.2021.3105745
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Jianfei Dong
中科院分区：
文献类型：
--
作者：
Jianfei Dong
摘要-迭代学习控制(ILC)依靠有限时间区间输出预测器来确定每次试验的输出轨迹。稳健的学习控制旨在对预报器中的不确定性进行建模，并保证学习过程的收敛受到此类模型误差的影响。尽管有大量的文献。在ILCS中，用随机性来参数化不确定性。从系统I/O数据中识别的预测器参数中的误差，从而使ILC健壮尚未成为目标。这项工作致力于以数据驱动的方式解决这样的问题。主要贡献有两方面。首先，针对LTI系统提出了一种数据驱动的迭代学习控制方法。建立了预测器矩阵中的误差与系统的随机扰动之间的关系。然后将其鲁棒单调收敛(RMC)与包含预测器不确定性的闭环系统学习增益矩阵联系起来，并基于该增益矩阵的闭合形式期望乘以其自身的转置，即在均方意义上(MS-RMC)。其次，将数据驱动的迭代学习控制和MS-RMC分析推广到非线性Hammerstein-Wiener(H-W)系统，最后通过大量的仿真验证了所提方法的优越性，它们的收敛性能和跟踪性能与随机参数和不确定性无关。
Abstract—Iterative learning control (ILC) relies on a finitetime.interval output predictor to determine the output trajectory.in each trial. Robust ILCs intend to model the uncertainties in.the predictor and to guarantee the convergence of the learning.process subject to such model errors. Despite the vast literature.in ILCs, parameterizing the uncertainties with the stochastic.errors in the predictor parameters identified from system I/O.data and thus robustifying the ILC have not yet been targeted..This work is devoted to solving such problems in a data-driven.fashion. The main contributions are two-fold. First, a data-driven.ILC method is developed for LTI systems. The relationship is.established between the errors in the predictor matrix and the.stochastic disturbances to the system. Its robust monotonic convergence.(RMC) is then linked with the closed-loop learning gain.matrix that contains the predictor uncertainties and is analyzed.based on a closed-form expectation of this gain matrix multiplied.with its own transpose, that is, in a mean-square sense.(MS-RMC). Second, the data-driven ILC and MS-RMC analysis.are extended to nonlinear Hammerstein–Wiener (H-W) systems..The advantages of the proposed methods are finally verified via.extensive simulations in terms of their convergence and uncorrelated.tracking performance with the stochastic parametric.uncertainties.
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