An algorithm for robust explicit/multi-parametric model predictive control

A new algorithm for robust explicit/multi-parametric Model Predictive Control (MPC) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Programming and Robust Optimization, the proposed algorithm features, (i) a DP reformulati...

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Published in:Automatica (Oxford) Vol. 49; no. 2; pp. 381 - 389
Main Authors: Kouramas, K.I., Panos, C., Faísca, N.P., Pistikopoulos, E.N.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.02.2013
Elsevier
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ISSN:0005-1098, 1873-2836
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Abstract A new algorithm for robust explicit/multi-parametric Model Predictive Control (MPC) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Programming and Robust Optimization, the proposed algorithm features, (i) a DP reformulations of the MPC optimization problem, (ii) a robust reformulation of the constraints, and (iii) a multi-parametric programming step, where the control variables are obtained as explicit functions of the state variable, such that the state and input constraints are satisfied for all admissible values of the uncertainty. A key feature of the proposed procedure is that, as opposed to previous methods, it only solves a convex multi-parametric programming problem for each stage of the DP procedure.
AbstractList A new algorithm for robust explicit/multi-parametric Model Predictive Control (MPC) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Programming and Robust Optimization, the proposed algorithm features, (i) a DP reformulations of the MPC optimization problem, (ii) a robust reformulation of the constraints, and (iii) a multi-parametric programming step, where the control variables are obtained as explicit functions of the state variable, such that the state and input constraints are satisfied for all admissible values of the uncertainty. A key feature of the proposed procedure is that, as opposed to previous methods, it only solves a convex multi-parametric programming problem for each stage of the DP procedure.
A new algorithm for robust explicit/multi-parametric Model Predictive Control (MPC) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Programming and Robust Optimization, the proposed algorithm features, (i) a DP reformulations of the MPC optimization problem, (ii) a robust reformulation of the constraints, and (iii)ANBa multi-parametric programming step, where the control variables are obtained as explicit functions of the state variable, such that the state and input constraints are satisfied for all admissible values of the uncertainty. A key feature of the proposed procedure is that, as opposed to previous methods, it only solves a convex multi-parametric programming problem for each stage of the DP procedure.
Author Kouramas, K.I.
Faísca, N.P.
Panos, C.
Pistikopoulos, E.N.
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Issue 2
Keywords Robust control
Explicit/multi-parametric MPC
Model predictive control
Multi-parametric programming
Multimodel control
Parametric programming
Convex programming
Optimization
Uncertain system
Discrete time
System identification
Dynamic programming
State constraint
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Snippet A new algorithm for robust explicit/multi-parametric Model Predictive Control (MPC) for uncertain, linear discrete-time systems is proposed. Based on previous...
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SubjectTerms Adaptative systems
Algorithms
Applied sciences
Computer science; control theory; systems
Control theory. Systems
Dynamical systems
Exact sciences and technology
Explicit/multi-parametric MPC
Mathematical models
Model predictive control
Modelling and identification
Multi-parametric programming
Optimal control
Optimization
Predictive control
Programming
Robust control
State variable
Uncertainty
Title An algorithm for robust explicit/multi-parametric model predictive control
URI https://dx.doi.org/10.1016/j.automatica.2012.11.035
https://www.proquest.com/docview/1315671394
Volume 49
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