论文标题
在线学习干扰控制风险感受控制：不到一分钟的数据的风险感知飞行
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
论文作者
论文摘要
安全至关重要的风险控制控制的最新进展是基于对系统可能面临的干扰知识的了解。本文提出了一种在风险感知的情况下有效地在线学习这些干扰的方法。首先，我们介绍了一种危险的概念，这是一种随机过程的风险度量，该过程扩展了风险的价值 - 在风险感知控制的控制社区中，常用的风险措施。其次，我们将模型与真实系统演化之间状态差异的规范建模为标量值的随机过程，并通过高斯过程回归确定与其表面风险的上限。第三，我们提供理论结果，以根据在系统操作过程中收集的数据集可验证的轻度假设，以拟验证的假设为基础。最后，我们通过增强无人机的控制器来验证我们的过程，并在收集不到一分钟的操作数据后通过我们的风险感知方法来提高性能。
Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data.
