Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes

Autor:	Gahlawat, Aditya, Lakshmanan, Arun, Song, Lin, Patterson, Andrew, Wu, Zhuohuan, Hovakimyan, Naira, Theodorou, Evangelos
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning Computer Science - Robotics Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted to Learning for Dynamics and Control (L4DC) Conference, 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2009.03864 Zobrazit plný text záznamu View this record from Arxiv