Taylor, Andrew J. and Dorobantu, Victor D. and Dean, Sarah and Recht, Benjamin and Yue, Yisong and Ames, Aaron D. (2021) Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty. In: 2021 60th IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 6469-6476. ISBN 978-1-6654-3659-5. https://resolver.caltech.edu/CaltechAUTHORS:20210120-165235061
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Abstract
Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust control synthesis in the presence of model uncertainty using Control Certificate Functions (CCFs), resulting in a convex optimization based controller for achieving properties like stability and safety. An important benefit of our framework is nuanced data-dependent guarantees, which in principle can yield sample-efficient data collection approaches that need not fully determine the input-to-state relationship. This work serves as a starting point for addressing important questions at the intersection of nonlinear control theory and non-parametric learning, both theoretical and in application. We demonstrate the efficiency of the proposed method with respect to input data in simulation with an inverted pendulum in multiple experimental settings.
Item Type: | Book Section | ||||||||||
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Additional Information: | © 2021 IEEE. | ||||||||||
DOI: | 10.1109/CDC45484.2021.9683511 | ||||||||||
Record Number: | CaltechAUTHORS:20210120-165235061 | ||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20210120-165235061 | ||||||||||
Official Citation: | A. J. Taylor, V. D. Dorobantu, S. Dean, B. Recht, Y. Yue and A. D. Ames, "Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty," 2021 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 6469-6476, doi: 10.1109/CDC45484.2021.9683511 | ||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||
ID Code: | 107611 | ||||||||||
Collection: | CaltechAUTHORS | ||||||||||
Deposited By: | George Porter | ||||||||||
Deposited On: | 21 Jan 2021 15:38 | ||||||||||
Last Modified: | 15 Feb 2022 23:25 |
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