论文标题
通过高斯流程和Luenberger内部模型进行数据驱动的输出调节
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models
论文作者
论文摘要
本文通过提出基于学习的基于学习的内部模型设计策略来处理多变量非线性系统的自适应输出调节问题。该方法基于基于非线性Luenberger观察者理论的最近提出的自适应内部模型设计技术，并将适应方作为概率回归问题接近。特别是，高斯工艺先验被用来应对学习问题。与该领域的先前方法不同，这里仅需要关于朋友结构的粗略假设，这使得提出的方法适用于外部系统高度不确定的应用。本文在已达到的法规误差和数值模拟上介绍了绩效界限，以表明所提出的方法的表现如何优于先前的方法。
This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design techniques based on the theory of nonlinear Luenberger observers, and the adaptation side is approached as a probabilistic regression problem. In particular, Gaussian process priors are employed to cope with the learning problem. Unlike the previous approaches in the field, here only coarse assumptions about the friend structure are required, making the proposed approach suitable for applications where the exosystem is highly uncertain. The paper presents performance bounds on the attained regulation error and numerical simulations showing how the proposed method outperforms previous approaches.
