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arXiv · 2011.01924

A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers

Dominic Liao-McPherson·Terrence Skibik·Torbjørn Cunis·Ilya Kolmanovsky·Marco M. Nicotra
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Abstract

This paper proposes a method for enlarging the region of attraction of Linear Model Predictive Controllers (MPC) when tracking piecewise-constant references in the presence of pointwise-in-time constraints. It consists of an add-on unit, the Feasibility Governor (FG), that manipulates the reference command so as to ensure that the optimal control problem that underlies the MPC feedback law remains feasible. Offline polyhedral projection algorithms based on multi-objective linear programming are employed to compute the set of feasible states and reference commands. Online, the action of the FG is computed by solving a convex quadratic program. The closed-loop system is shown to satisfy constraints, be asymptotically stable, exhibit zero-offset tracking, and display finite-time convergence of the reference.

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Dominic Liao-McPherson, Terrence Skibik, Torbjørn Cunis, Ilya Kolmanovsky, Marco M. Nicotra. 2020-11-03. A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers. https://doi.org/10.1109/tac.2021.3123224

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