低次元モデルを用いた入力制約下の安全臨界制御
Safety-Critical Control with Bounded Inputs via Reduced Order Models
複雑なロボットの安全制御を、低次元モデルとバックアップ集合法を組み合わせて実現し、入力制約や外乱があっても全次元系の安全性を保証する枠組みを提案した。
著者: Tamas G. Molnar, Aaron D. Ames
分類: eess.SY, cs.RO, cs.SY, math.DS
原文アブストラクト
Guaranteeing safe behavior on complex autonomous systems -- from cars to walking robots -- is challenging due to the inherently high dimensional nature of these systems and the corresponding complex models that may be difficult to determine in practice. With this as motivation, this paper presents a safety-critical control framework that leverages reduced order models to ensure safety on the full order dynamics -- even when these models are subject to disturbances and bounded inputs (e.g., actuation limits). To handle input constraints, the backup set method is reformulated in the context of reduced order models, and conditions for the provably safe behavior of the full order system are derived. Then, the input-to-state safe backup set method is introduced to provide robustness against discrepancies between the reduced order model and the actual system. Finally, the proposed framework is demonstrated in high-fidelity simulation, where a quadrupedal robot is safely navigated around an obstacle with legged locomotion by the help of the unicycle model.
