Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation

Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation

This paper presents a contraction-based learning control architecture that allows for using model learning tools to learn matched model uncertainties while guaranteeing trajectory tracking performance during the learning transients. The architecture relies on a disturbance estimator to estimate the...

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Bibliographic Details
Main Authors: Pan Zhao, Ziyao Guo, Yikun Cheng, Aditya Gahlawat, Hyungsoo Kang, Naira Hovakimyan
Format: Article
Language:English
Published: MDPI AG 2024-06-01
Series:Robotics
Subjects:
robust control
decision-making under uncertainty
machine learning for control
robot safety
Online Access:https://www.mdpi.com/2218-6581/13/7/99

Internet

https://www.mdpi.com/2218-6581/13/7/99

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