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Output-Lifted Learning Model Predictive Control

Nair, Siddharth H. and Rosolia, Ugo and Borrelli, Francesco (2020) Output-Lifted Learning Model Predictive Control. . (Unpublished)

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We propose a computationally efficient Learning Model Predictive Control (LMPC) scheme for constrained optimal control of a class of nonlinear systems where the state and input can be reconstructed using lifted outputs. For the considered class of systems, we show how to use historical trajectory data collected during iterative tasks to construct a convex value function approximation along with a convex safe set in a lifted space of virtual outputs. These constructions are iteratively updated with historical data and used to synthesize predictive control policies. We show that the proposed strategy guarantees recursive constraint satisfaction, asymptotic stability and non-decreasing closed-loop performance at each policy update. Finally, simulation results demonstrate the effectiveness of the proposed strategy on a piecewise affine (PWA) system, kinematic unicycle and bilinear DC motor.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Rosolia, Ugo0000-0002-1682-0551
Borrelli, Francesco0000-0001-8919-6430
Alternate Title:Output-Lifted Learning Model Predictive Control for Flat Systems
Additional Information:We would like to thank Koushil Sreenath for helpful discussions. This work was also sponsored by the Office of Naval Research. 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.
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Office of Naval Research (ONR)UNSPECIFIED
Record Number:CaltechAUTHORS:20210716-225828441
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109898
Deposited By: George Porter
Deposited On:16 Jul 2021 23:18
Last Modified:16 Jul 2021 23:18

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