Stabilizable regions of receding horizon predictive control with input constraints

Stabilizable regions of receding horizon predictive control (RHPC) with input constraints are examined. A feasible region of states, which is spanned by eigenvectors of the closed-loop system with a stabilizing feedback gain, is derived in conjunction with input constraints. For states in this regio...

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Main Authors: Lee, Y, Kouvaritakis, B
Format: Journal article
Language:English
Published: 1999
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author Lee, Y
Kouvaritakis, B
author_facet Lee, Y
Kouvaritakis, B
author_sort Lee, Y
collection OXFORD
description Stabilizable regions of receding horizon predictive control (RHPC) with input constraints are examined. A feasible region of states, which is spanned by eigenvectors of the closed-loop system with a stabilizing feedback gain, is derived in conjunction with input constraints. For states in this region, the feasibility of state feedback is guaranteed with the corresponding feedback gain. It is shown that an RHPC scheme with adequate finite terminal weights can guarantee stability for any initial state which can be steered into this region using finite number of control moves in the presence of input saturation. This methodology results in feasible regions which are infinite (in certain directions) even in the case of open-loop unstable systems. It is shown that the proposed feasible regions are larger than the ellipsoidal regions which were suggested in earlier works. We formulated the optimization problem in LMI so that it can be solved by semidefinite programming. © 1999 Elsevier Science B.V. All rights reserved.
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spelling oxford-uuid:9a423a6b-7fc0-4dde-88f3-88176b0e581f2022-03-27T00:20:05ZStabilizable regions of receding horizon predictive control with input constraintsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9a423a6b-7fc0-4dde-88f3-88176b0e581fEnglishSymplectic Elements at Oxford1999Lee, YKouvaritakis, BStabilizable regions of receding horizon predictive control (RHPC) with input constraints are examined. A feasible region of states, which is spanned by eigenvectors of the closed-loop system with a stabilizing feedback gain, is derived in conjunction with input constraints. For states in this region, the feasibility of state feedback is guaranteed with the corresponding feedback gain. It is shown that an RHPC scheme with adequate finite terminal weights can guarantee stability for any initial state which can be steered into this region using finite number of control moves in the presence of input saturation. This methodology results in feasible regions which are infinite (in certain directions) even in the case of open-loop unstable systems. It is shown that the proposed feasible regions are larger than the ellipsoidal regions which were suggested in earlier works. We formulated the optimization problem in LMI so that it can be solved by semidefinite programming. © 1999 Elsevier Science B.V. All rights reserved.
spellingShingle Lee, Y
Kouvaritakis, B
Stabilizable regions of receding horizon predictive control with input constraints
title Stabilizable regions of receding horizon predictive control with input constraints
title_full Stabilizable regions of receding horizon predictive control with input constraints
title_fullStr Stabilizable regions of receding horizon predictive control with input constraints
title_full_unstemmed Stabilizable regions of receding horizon predictive control with input constraints
title_short Stabilizable regions of receding horizon predictive control with input constraints
title_sort stabilizable regions of receding horizon predictive control with input constraints
work_keys_str_mv AT leey stabilizableregionsofrecedinghorizonpredictivecontrolwithinputconstraints
AT kouvaritakisb stabilizableregionsofrecedinghorizonpredictivecontrolwithinputconstraints