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Control of Unknown Nonlinear Systems with Linear Time-Varying MPC

Papadimitriou, Dimitris and Rosolia, Ugo and Borrelli, Francesco (2020) Control of Unknown Nonlinear Systems with Linear Time-Varying MPC. In: 2020 59th IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 2258-2263. ISBN 9781728174471.

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We present a Model Predictive Control (MPC) strategy for unknown input-affine nonlinear dynamical systems. A non-parametric method is used to estimate the nonlinear dynamics from observed data. The estimated nonlinear dynamics are then linearized over time-varying regions of the state space to construct an Affine Time-Varying (ATV) model. Error bounds arising from the estimation and linearization procedure are computed by using sampling techniques. The ATV model and the uncertainty sets are used to design a robust Model Predictive Controller (MPC) which guarantees safety for the unknown system with high probability. A simple nonlinear example demonstrates the effectiveness of the approach where commonly used estimation and linearization methods fail.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Rosolia, Ugo0000-0002-1682-0551
Borrelli, Francesco0000-0001-8919-6430
Additional Information:© 2020 IEEE. This work was also sponsored by the Office of Naval Research grant ONR-N00014-18-1-2833. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Office of Naval Research or the US government.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-18-1-2833
Record Number:CaltechAUTHORS:20210121-152558095
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Official Citation:D. Papadimitriou, U. Rosolia and F. Borrelli, "Control of Unknown Nonlinear Systems with Linear Time-Varying MPC," 2020 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Korea (South), 2020, pp. 2258-2263, doi: 10.1109/CDC42340.2020.9304441
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107643
Deposited By: George Porter
Deposited On:21 Jan 2021 23:59
Last Modified:16 Nov 2021 19:04

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