标题： 无需先验知识的鲁棒无模型学习与控制 显示英文标题

标题： Robust Model-Free Learning and Control without Prior Knowledge

摘要： 我们提出了一种简单的无模型控制算法，该算法能够鲁棒地学习并稳定一个未知的离散时间线性系统，在完全控制和状态反馈下受到任意有界扰动和噪声序列的影响。 控制器不需要任何关于系统动态、扰动或噪声的先验知识，但仍能保证鲁棒稳定性，并提供状态和输入轨迹的渐近和最坏情况边界。 据我们所知，这是第一个具有这种鲁棒稳定性保证的无模型算法，而无需对系统做出任何先验假设。 我们想强调在鲁棒稳定性分析中采用的新凸几何方法，这在我们的结果中起到了关键推动作用。 我们将通过仿真结果进行总结，这些结果表明，尽管算法具有通用性和简单性，但控制器展示了良好的闭环性能。 显示英文摘要

摘要： 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.