论文标题
通过嘈杂的测量来支持学习的健壮控制
Learning-Enabled Robust Control with Noisy Measurements
论文作者
论文摘要
我们提出了一种有限的$ \ ell_2 $ gain自适应控制的建设性方法，具有嘈杂的测量，用于线性时间不变的标量系统，其不确定参数属于有限集。收益约束是指闭环系统，包括学习过程。该方法基于前向动态编程，以构建一个有限维信息状态，该信息由$ \ Mathcal H_ \ indcal h_ \ indy \ infty $ - 观察者与递归计算的性能度量配对。我们不假定稳定控制器的先验知识。
We present a constructive approach to bounded $\ell_2$-gain adaptive control with noisy measurements for linear time-invariant scalar systems with uncertain parameters belonging to a finite set. The gain bound refers to the closed-loop system, including the learning procedure. The approach is based on forward dynamic programming to construct a finite-dimensional information state consisting of $\mathcal H_\infty$-observers paired with a recursively computed performance metric. We do not assume prior knowledge of a stabilizing controller.
