论文标题
对基于模型不匹配的基于在线梯度下降的迭代学习控制的遗憾分析
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
论文作者
论文摘要
在迭代学习控制（ILC）中，每次迭代中都会根据部分模型知识和过去的测量来生成一系列馈电控制动作，目的是将系统转向所需的参考轨迹。这是在这里作为一项在线学习任务进行框架的，决策者通过解决一系列优化问题的顺序决策，仅对成本功能有部分知识。在建立了此连接后，在在线学习中的动态和静态遗憾的设置中分析了使用不应收入用梯度信息的基于在线梯度的方案的性能。进一步研究了该方案的基本局限性及其与适应机制的整合，然后在基准ILC问题上进行数值模拟。
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the system toward a desired reference trajectory. This is framed here as an online learning task, where the decision-maker takes sequential decisions by solving a sequence of optimization problems having only partial knowledge of the cost functions. Having established this connection, the performance of an online gradient-descent based scheme using inexact gradient information is analyzed in the setting of dynamic and static regret, standard measures in online learning. Fundamental limitations of the scheme and its integration with adaptation mechanisms are further investigated, followed by numerical simulations on a benchmark ILC problem.
