论文标题
确定性基于感知的控制
Certainty Equivalent Perception-Based Control
论文作者
论文摘要
为了证明性能和安全性，反馈控制需要精确表征传感器错误。在本文中，当传感器以解决监督的学习问题为特征时，我们为此类反馈系统提供保证。我们显示在动态启动的致密采样方案下，在非参数内核回归上绑定的均匀误差。这允许在闭环中使用回归器进行路线跟踪的子临时性的有限时间收敛率。我们通过简化的无人机和自动驾驶示例来证明我们的模拟结果。
In order to certify performance and safety, feedback control requires precise characterization of sensor errors. In this paper, we provide guarantees on such feedback systems when sensors are characterized by solving a supervised learning problem. We show a uniform error bound on nonparametric kernel regression under a dynamically-achievable dense sampling scheme. This allows for a finite-time convergence rate on the sub-optimality of using the regressor in closed-loop for waypoint tracking. We demonstrate our results in simulation with simplified unmanned aerial vehicle and autonomous driving examples.
