论文标题
通过高斯过程的自适应非线性调节
Adaptive Nonlinear Regulation via Gaussian Process
论文作者
论文摘要
本文通过提出基于学习的基于内部模型的设计策略来处理非线性系统的输出调节问题。我们从最近在[1]中提出的自适应内部模型设计技术中借用，并通过高斯过程回归剂扩展它。基于学习的适应性是通过遵循“事件触发”逻辑来执行的，以便使用混合工具来分析所得的闭环系统。与[1]中提出的方法不同，该方法应该属于特定的有限维模型集，在这里，我们只需要理想的稳态控制动作的平滑度。本文还提供了数值模拟，显示了所提出的方法如何优于先前的方法。
The paper deals with the problem of output regulation of nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. We borrow from the adaptive internal model design technique recently proposed in [1] and extend it by means of a Gaussian process regressor. The learning-based adaptation is performed by following an "event-triggered" logic so that hybrid tools are used to analyse the resulting closed-loop system. Unlike the approach proposed in [1] where the friend is supposed to belong to a specific finite-dimensional model set, here we only require smoothness of the ideal steady-state control action. The paper also presents numerical simulations showing how the proposed method outperforms previous approaches.
