Convex MPC for exclusion constraints

This article develops model predictive control for exclusion constraints with a priori guaranteed strong system theoretic properties, which is implementable via computationally highly efficient, strictly convex quadratic programming. The proposed approach deploys safe tubes in order to ensure intrin...

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Bibliographic Details
Published in:Automatica (Oxford) 2021-05, Vol.127, p.109502, Article 109502
Main Authors: Raković, Saša V., Zhang, Sixing, Hao, Yanye, Dai, Li, Xia, Yuanqing
Format: Article
Language:English
Subjects:
Convex optimization
Exclusion constraints
Linear systems
Model predictive control
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Summary:This article develops model predictive control for exclusion constraints with a priori guaranteed strong system theoretic properties, which is implementable via computationally highly efficient, strictly convex quadratic programming. The proposed approach deploys safe tubes in order to ensure intrinsically nonconvex exclusion constraints via closed polyhedral constraints. A safe tube is constructed by utilizing the separation theorem for convex sets, and it is practically obtained from the solution of a strictly convex quadratic programming problem. A safe tube is deployed to efficiently optimize a predicted finite horizon control process via another strictly convex quadratic programming problem.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2021.109502