ガウス過程を用いた収縮L1適応制御
Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes
収縮理論ベースのL1適応制御とガウス過程回帰を組み合わせ、不確実性下でも学習中を通じて安全性を保証しながら制御と学習を同時に行うフレームワークを提案した論文。
著者: Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos Theodorou
分類: eess.SY, cs.LG, cs.RO, cs.SY, math.OC
原文アブストラクト
We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties. The two main constituents are contraction theory-based $\mathcal{L}_1$ ($\mathcal{CL}_1$) control and Bayesian learning in the form of Gaussian process (GP) regression. The $\mathcal{CL}_1$ controller ensures that control objectives are met while providing safety certificates. Furthermore, $\mathcal{CL}_1$-$\mathcal{GP}$ incorporates any available data into a GP model of uncertainties, which improves performance and enables the motion planner to achieve optimality safely. This way, the safe operation of the system is always guaranteed, even during the learning transients. We provide a few illustrative examples for the safe learning and control of planar quadrotor systems in a variety of environments.
