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 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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This article presents a model predictive control method for exclusion constraints that ensures strong system theoretic properties and is implemented using computationally highly efficient, strictly convex quadratic programming. The approach utilizes safe tubes to handle intrinsically nonconvex exclusion constraints through closed polyhedral constraints, constructed using the separation theorem for convex sets. The safe tube is obtained practically from the solution of a strictly convex quadratic programming problem and is used to optimize a predicted finite horizon control process efficiently via another strictly convex quadratic programming problem.