Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control

Recently introduced parameterized tube model predictive control (PTMPC) supersedes the robust model predictive control (RMPC) using the affine in the past disturbances control policy, and is, in certain cases, equivalent to RMPC utilizing dynamic programming. PTMPC online implementation reduces to a...

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المؤلفون الرئيسيون: Rakovic, S, Munoz-Carpintero, D, Cannon, M, Kouvaritakis, B, IEEE
التنسيق: Conference item
منشور في: 2012
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author Rakovic, S
Munoz-Carpintero, D
Cannon, M
Kouvaritakis, B
IEEE
author_facet Rakovic, S
Munoz-Carpintero, D
Cannon, M
Kouvaritakis, B
IEEE
author_sort Rakovic, S
collection OXFORD
description Recently introduced parameterized tube model predictive control (PTMPC) supersedes the robust model predictive control (RMPC) using the affine in the past disturbances control policy, and is, in certain cases, equivalent to RMPC utilizing dynamic programming. PTMPC online implementation reduces to a standard convex optimization for which the numbers of the decision variables and equality and inequality constraints grow quadratically with respect to the prediction horizon N. This paper considers the reduction of computational complexity to linear growth by allowing a part of the computation to be performed offline. The suggested offline simplifications preserve, to a high degree, the desirable system theoretic properties associated with PTMPC. © 2012 IEEE.
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spelling oxford-uuid:21e1588c-d9d7-4294-816c-ca898092e0842022-03-26T11:35:50ZOffline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive ControlConference itemhttp://purl.org/coar/resource_type/c_5794uuid:21e1588c-d9d7-4294-816c-ca898092e084Symplectic Elements at Oxford2012Rakovic, SMunoz-Carpintero, DCannon, MKouvaritakis, BIEEERecently introduced parameterized tube model predictive control (PTMPC) supersedes the robust model predictive control (RMPC) using the affine in the past disturbances control policy, and is, in certain cases, equivalent to RMPC utilizing dynamic programming. PTMPC online implementation reduces to a standard convex optimization for which the numbers of the decision variables and equality and inequality constraints grow quadratically with respect to the prediction horizon N. This paper considers the reduction of computational complexity to linear growth by allowing a part of the computation to be performed offline. The suggested offline simplifications preserve, to a high degree, the desirable system theoretic properties associated with PTMPC. © 2012 IEEE.
spellingShingle Rakovic, S
Munoz-Carpintero, D
Cannon, M
Kouvaritakis, B
IEEE
Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control
title Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control
title_full Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control
title_fullStr Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control
title_full_unstemmed Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control
title_short Offline Tube Design for Efficient Implementation of Parameterized Tube Model Predictive Control
title_sort offline tube design for efficient implementation of parameterized tube model predictive control
work_keys_str_mv AT rakovics offlinetubedesignforefficientimplementationofparameterizedtubemodelpredictivecontrol
AT munozcarpinterod offlinetubedesignforefficientimplementationofparameterizedtubemodelpredictivecontrol
AT cannonm offlinetubedesignforefficientimplementationofparameterizedtubemodelpredictivecontrol
AT kouvaritakisb offlinetubedesignforefficientimplementationofparameterizedtubemodelpredictivecontrol
AT ieee offlinetubedesignforefficientimplementationofparameterizedtubemodelpredictivecontrol