Feasibility Governor for Linear Model Predictive Control
arXiv:2103.05130 · doi:10.23919/ACC50511.2021.9483054
Abstract
This paper introduces the Feasibility Governor (FG): an add-on unit that enlarges the region of attraction of Model Predictive Control by manipulating the reference to ensure that the underlying optimal control problem remains feasible. The FG is developed for linear systems subject to polyhedral state and input constraints. Offline computations using polyhedral projection algorithms are used to construct the feasibility set. Online implementation relies on the solution of a convex quadratic program that guarantees recursive feasibility. The closed-loop system is shown to satisfy constraints, achieve asymptotic stability, and exhibit zero-offset tracking.
Accepted in 2021 American Control Conference (ACC), May 25 to 28, 2021. arXiv admin note: substantial text overlap with arXiv:2011.01924