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Learning-based Adaptive Control using Contraction Theory

Tsukamoto, Hiroyasu and Chung, Soon-Jo and Slotine, Jean-Jacques (2021) Learning-based Adaptive Control using Contraction Theory. In: 2021 60th IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 2533-2538. ISBN 978-1-6654-3659-5.

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Adaptive control is subject to stability and performance issues when a learned model is used to enhance its performance. This paper thus presents a deep learning-based adaptive control framework for nonlinear systems with multiplicatively-separable parametrization, called adaptive Neural Contraction Metric (aNCM). The aNCM approximates real-time optimization for computing a differential Lyapunov function and a corresponding stabilizing adaptive control law by using a Deep Neural Network (DNN). The use of DNNs permits real-time implementation of the control law and broad applicability to a variety of nonlinear systems with parametric and nonparametric uncertainties. We show using contraction theory that the aNCM ensures exponential boundedness of the distance between the target and controlled trajectories in the presence of parametric uncertainties of the model, learning errors caused by aNCM approximation, and external disturbances. Its superiority to the existing robust and adaptive control methods is demonstrated using a cart-pole balancing model.

Item Type:Book Section
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URLURL TypeDescription Paper ItemCode
Chung, Soon-Jo0000-0002-6657-3907
Slotine, Jean-Jacques0000-0002-7161-7812
Alternate Title:Learning-based Adaptive Control via Contraction Theory
Additional Information:© 2021 IEEE. This work was in part funded by the Jet Propulsion Laboratory, California Institute of Technology. Code:
Funding AgencyGrant Number
Record Number:CaltechAUTHORS:20210510-141344204
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Official Citation:H. Tsukamoto, S. -J. Chung and J. -J. Slotine, "Learning-based Adaptive Control using Contraction Theory," 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 2533-2538, doi: 10.1109/CDC45484.2021.9683435
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109052
Deposited By: George Porter
Deposited On:10 May 2021 21:40
Last Modified:16 Feb 2022 18:00

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