Minimum time learning model predictive control
- Creators
- Rosolia, Ugo
- Borrelli, Francesco
Abstract
In this paper we present a Learning Model Predictive Control (LMPC) strategy for linear and nonlinear time optimal control problems. Our work builds on existing LMPC methodologies and it guarantees finite time convergence properties for the closed-loop system. We show how to construct a time varying safe set and terminal cost function using closed-loop data. The resulting LMPC policy is time varying and it guarantees recursive constraint satisfaction and non-decreasing performance. Computational efficiency is obtained by convexifing the time-varying safe set and time-varying terminal cost function. We demonstrate that, for a class of nonlinear system and convex constraints, the convex LMPC formulation guarantees recursive constraint satisfaction and nondecreasing performance. Finally, we illustrate the effectiveness of the proposed strategies on minimum time obstacle avoidance and racing examples.
Additional Information
© 2020 John Wiley & Sons. Issue Online: 18 November 2021; Version of Record online: 22 October 2020; Manuscript accepted: 30 September 2020; Manuscript revised: 27 September 2020; Manuscript received: 16 October 2019. The authors would like to thank Nicola Scianca from Sapienza University of Roma for the interesting discussions on repetitive LMPC and reviewers for helpful suggestions. Some of the research described in this review was funded by the Hyundai Center of Excellence at the University of California, Berkeley. This work was also sponsored by the Office of Naval Research grant 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.Attached Files
Accepted Version - 1911.09239.pdf
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Additional details
- Eprint ID
- 106246
- Resolver ID
- CaltechAUTHORS:20201023-093316949
- Hyundai
- N00014‐18‐1‐2833
- Office of Naval Research (ONR)
- Created
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2020-10-23Created from EPrint's datestamp field
- Updated
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2021-11-23Created from EPrint's last_modified field