Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
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DOI：
10.48550/arxiv.2212.06253
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Prithvi Akella;Skylar X. Wei;J. Burdick;A. Ames
中科院分区：
文献类型：
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作者：
Prithvi Akella;Skylar X. Wei;J. Burdick;A. Ames
安全关键风险感知控制的最新进展是基于对系统可能面临的干扰的先验知识。本文提出了一种方法，有效地学习这些干扰在线，在风险意识的背景下。首先，我们引入了风险表面的概念，随机过程的风险度量，扩展了风险价值-风险意识控制社区中常用的风险度量。其次，我们将模型与真实系统演化之间的状态差异的范数建模为标量值随机过程，并通过高斯过程回归确定其风险面的上界。第三，我们提供的理论结果的准确性，我们的拟合表面受到温和的假设，是可验证的系统运行过程中收集的数据集。最后，我们通过增强无人机的控制器来实验验证我们的程序，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突出显示性能提高。
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.