论文标题
没有先验知识的强大的无模型学习和控制
Robust Model-Free Learning and Control without Prior Knowledge
论文作者
论文摘要
我们提出了一种简单的无模型控制算法，该算法能够鲁棒学习和稳定未知的离散时间线性系统，并具有完全控制和状态反馈，但要受任意界定的干扰和噪声序列。控制器不需要对系统动力学，干扰或噪声的任何先验知识，但是它可以保证稳健的稳定性，并在状态和输入轨迹上提供渐近和最坏情况的界限。据我们所知，这是第一个具有强大稳定性保证的无模型算法，而无需对系统做出任何先前的假设。我们想强调针对稳健稳定性分析采用的新的基于凸几何的方法，这是我们结果的关键推动力。我们将以模拟结果的结论表明，尽管有一般性和简单性，但控制器表现出良好的闭环性能。
We present a simple model-free control algorithm that is able to robustly learn and stabilize an unknown discrete-time linear system with full control and state feedback subject to arbitrary bounded disturbance and noise sequences. The controller does not require any prior knowledge of the system dynamics, disturbances, or noise, yet it can guarantee robust stability and provides asymptotic and worst-case bounds on the state and input trajectories. To the best of our knowledge, this is the first model-free algorithm that comes with such robust stability guarantees without the need to make any prior assumptions about the system. We would like to highlight the new convex geometry-based approach taken towards robust stability analysis which served as a key enabler in our results. We will conclude with simulation results that show that despite the generality and simplicity, the controller demonstrates good closed-loop performance.
