Robust Model-Free Learning and Control without Prior Knowledge
Robust Model-Free Learning and Control without Prior Knowledge
复制标题
DOI：
10.1109/cdc40024.2019.9029986
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
Dimitar Ho;J. Doyle
中科院分区：
文献类型：
--
作者：
Dimitar Ho;J. Doyle
我们提出了一种简单的无模型控制算法，该算法能够稳健地学习和稳定未知的离散时间线性系统，并具有受任意有界扰动和噪声序列影响的完全控制和状态反馈。该控制器不需要任何系统动力学、干扰或噪声的先验知识，但可以保证状态偏差的鲁棒稳定性、统一渐近边界和统一最坏情况边界。我们想重点介绍的不是算法本身，而是稳健稳定性分析所采用的新方法，该方法是提供所提出的稳定性和性能保证的关键推动因素。我们将以仿真结果作为结论，该结果表明，尽管控制器具有通用性和简单性，但仍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 discretetime 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 can guarantee robust stability, uniform asymptotic bounds and uniform worst-case bounds on the state-deviation. Rather than the algorithm itself, we would like to highlight the new approach taken towards robust stability analysis which served as a key enabler in providing the presented stability and performance guarantees. We will conclude with simulation results that show that despite the generality and simplicity, the controller demonstrates good closed-loop performance.