论文标题
控制设计的鲁棒性通过贝叶斯学习
Robustness of Control Design via Bayesian Learning
论文作者
论文摘要
在监督学习的领域中，贝叶斯学习在输入和参数扰动下显示出强大的预测能力。受这些发现的启发，我们演示了在控制搜索任务中贝叶斯学习的鲁棒性特性。我们试图找到一个线性控制器，该线性控制器稳定了一维开环不稳定的随机系统。我们比较了推断控制器的两种方法：第一个（确定性）假设对系统参数和状态的完美知识，第二个考虑了两者的不确定性，并采用贝叶斯学习来计算控制器的后验分布。
In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search task. We seek to find a linear controller that stabilizes a one-dimensional open-loop unstable stochastic system. We compare two methods to deduce the controller: the first (deterministic) one assumes perfect knowledge of system parameter and state, the second takes into account uncertainties in both and employs Bayesian learning to compute a posterior distribution for the controller.
