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Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty

Taylor, Andrew J. and Dorobantu, Victor D. and Dean, Sarah and Recht, Benjamin and Yue, Yisong and Ames, Aaron D. (2020) Towards Robust Data-Driven Control Synthesis for Nonlinear Systems with Actuation Uncertainty. . (Unpublished)

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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 validate the proposed method in simulation with an inverted pendulum in multiple experimental configurations.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Taylor, Andrew J.0000-0002-5990-590X
Recht, Benjamin0000-0002-0293-593X
Yue, Yisong0000-0001-9127-1989
Ames, Aaron D.0000-0003-0848-3177
Additional Information:Submitted to Learning for Dynamics & Control 2021 (L4DC).
Subject Keywords:robust control, data-driven, Lyapunov, barriers
Record Number:CaltechAUTHORS:20210120-165235061
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107611
Deposited By: George Porter
Deposited On:21 Jan 2021 15:38
Last Modified:21 Jan 2021 15:38

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