Minimum time learning model predictive control. (22nd October 2020)

• Record Type: Journal Article
• Authors: Rosolia, Ugo; Borrelli, Francesco
• Other Names: Müller Matthias A. guestEditor.; Allgöwer Frank guestEditor.
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 18(2021)
• Page Start: 8830
• Page End: 8854
• Publication Date: 2020-10-22
• Subjects: iterative improvement -- learning model predictive control -- minimum time -- predictive control; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5284
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: 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.