Learning to perform perfect tracking tasks based on measurement data is desirable in the controller design of systems operating repetitively. This motivates this article to seek an optimization-based design and analysis approach for data-driven learning control systems by focusing on iterative learning control (ILC) of repetitive systems with unknown nonlinear time-varying dynamics. It is shown that perfect output tracking can be realized with updating inputs, where no explicit model knowledge but only measured input–output data are leveraged. In particular, adaptive updating strategies are proposed to obtain parameter estimations of nonlinearities. A double-dynamics analysis approach is applied to establish ILC convergence, together with boundedness of input, output, and estimated parameters, which benefits from employing properties of nonnegative matrices. Simulations are implemented to verify the validity of our optimization-based adaptive ILC.

中文翻译：

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数据驱动学习控制的设计和分析：基于优化的方法


在重复操作的系统的控制器设计中，需要学习根据测量数据执行完美的跟踪任务。这促使本文通过关注具有未知非线性时变动力学的重复系统的迭代学习控制（ILC）来寻求一种用于数据驱动学习控制系统的基于优化的设计和分析方法。结果表明，可以通过更新输入来实现完美的输出跟踪，其中没有明确的模型知识，而仅利用测量的输入输出数据。特别是，提出了自适应更新策略来获得非线性参数估计。采用双动力学分析方法来建立 ILC 收敛性，以及输入、输出和估计参数的有界性，这得益于非负矩阵的性质。进行仿真以验证我们基于优化的自适应 ILC 的有效性。