%0 Conference Paper %1 Skibik2021acc %A Skibik, Terrence %A Liao-McPherson, Dominic %A Cunis, Torbjørn %A Kolmanovsky, Ilya %A Nicotra, Marco M. %B Proceedings of the American Control Conference %C New Orleans, LA %D 2021 %K %P 2329--2335 %R 10.23919/ACC50511.2021.9483054 %T Feasibility Governor for Linear Model Predictive Control %X 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. %@ 9781665441971

@inproceedings{Skibik2021acc, 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.}, added-at = {2023-01-10T09:33:37.000+0100}, address = {New Orleans, LA}, author = {Skibik, Terrence and Liao-McPherson, Dominic and Cunis, Torbj{\o}rn and Kolmanovsky, Ilya and Nicotra, Marco M.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/26ee81c3f8432749c62e3f685db3515f0/tcunis}, booktitle = {Proceedings of the American Control Conference}, doi = {10.23919/ACC50511.2021.9483054}, file = {:Users/cunis/Documents/Academics/Conferences/2021_ACC/Feasibility_Governor_for_Linear_Model_Predictive_Control.pdf:pdf}, interhash = {bcd98492bc2c83e446fbf2f0c14704fe}, intrahash = {6ee81c3f8432749c62e3f685db3515f0}, isbn = {9781665441971}, issn = {07431619}, keywords = {}, pages = {2329--2335}, timestamp = {2023-01-10T08:33:37.000+0100}, title = {{Feasibility Governor for Linear Model Predictive Control}}, year = 2021 }