Output-Lifted Learning Model Predictive Control
Abstract
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 the kinematic unicycle.
Additional Information
© 2021 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control. Available online 9 September 2021. This work has been sponsored by the Office of Naval Research.Attached Files
Published - 1-s2.0-S240589632101346X-main.pdf
Submitted - 2004.05173.pdf
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Additional details
- Alternative title
- Output-Lifted Learning Model Predictive Control for Flat Systems
- Eprint ID
- 109898
- Resolver ID
- CaltechAUTHORS:20210716-225828441
- Office of Naval Research (ONR)
- Created
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2021-07-16Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field