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Learning-based Adaptive Control via Contraction Theory

Tsukamoto, Hiroyasu and Chung, Soon-Jo and Slotine, Jean-Jacques (2021) Learning-based Adaptive Control via Contraction Theory. . (Unpublished)

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We present a new deep learning-based adaptive control framework for nonlinear systems with multiplicatively-separable parametric uncertainty, called an adaptive Neural Contraction Metric (aNCM). The aNCM uses a neural network model of an optimal adaptive contraction metric, the existence of which guarantees asymptotic stability and exponential boundedness of system trajectories under the parametric uncertainty. In particular, we exploit the concept of a Neural Contraction Metric (NCM) to obtain a nominal provably stable robust control policy for nonlinear systems with bounded disturbances, and combine this policy with a novel adaptation law to achieve stability guarantees. We also show that the framework is applicable to adaptive control of dynamical systems modeled via basis function approximation. Furthermore, the use of neural networks in the aNCM permits its real-time implementation, resulting in broad applicability to a variety of systems. Its superiority to the state-of-the-art is illustrated with a simple cart-pole balancing task.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemCode
Chung, Soon-Jo0000-0002-6657-3907
Slotine, Jean-Jacques0000-0002-7161-7812
Additional Information: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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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:10 May 2021 21:40

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