1分未満のデータでリスクを考慮した制御のための外乱オンライン学習
Learning Disturbances Online for Risk-Aware Control: Risk-Aware Flight with Less Than One Minute of Data
ドローンの制御において、モデルと実システムのずれを確率的に学習し、1分未満の運用データでリスクを考慮した安全な飛行を実現する手法を提案した。
著者: Prithvi Akella, Skylar X. Wei, Joel W. Burdick, Aaron D. Ames
分類: eess.SY, cs.IT, cs.LG, cs.RO, cs.SY, math.IT
原文アブストラクト
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.
