Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming
This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic pr...
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| Published in: | Automatica (Oxford) Vol. 47; no. 8; pp. 1638 - 1645 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.08.2011
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| ISSN: | 0005-1098, 1873-2836 |
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| Abstract | This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step. |
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| AbstractList | This work presents a new algorithm for solving the explicit/multi-parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step. |
| Author | Kouramas, K.I. Faísca, N.P. Panos, C. Pistikopoulos, E.N. |
| Author_xml | – sequence: 1 givenname: K.I. surname: Kouramas fullname: Kouramas, K.I. email: k.kouramas@imperial.ac.uk organization: Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK – sequence: 2 givenname: N.P. surname: Faísca fullname: Faísca, N.P. email: nuno.faisca@aspentech.com organization: AspenTech, C1 Reading International Bus Park, Reading, RG2 6DT, UK – sequence: 3 givenname: C. surname: Panos fullname: Panos, C. email: christos.panos08@imperial.ac.uk organization: Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK – sequence: 4 givenname: E.N. surname: Pistikopoulos fullname: Pistikopoulos, E.N. email: e.pistikopoulos@imperial.ac.uk organization: Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK |
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| Cites_doi | 10.1002/aic.11965 10.1016/j.automatica.2004.02.007 10.1007/s11590-007-0056-3 10.1109/TAC.2003.816984 10.23919/ECC.2001.7076033 10.1016/S1474-6670(17)34180-0 10.1016/S0098-1354(01)00739-6 10.1016/j.compchemeng.2010.07.032 10.1016/j.automatica.2005.04.017 10.1109/CDC.2002.1184260 10.1109/CDC.2004.1428785 10.1109/ACC.2000.876688 10.1016/S0005-1098(01)00174-1 10.1016/j.automatica.2003.08.011 10.1109/CDC.2003.1273110 10.1016/j.automatica.2005.10.015 10.1016/S0005-1098(02)00250-9 |
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| Keywords | Explicit Model Predictive Control Multi-parametric control Multi-parametric programming Dynamic programming Model Predictive Control Multimodel control Parametric programming Global optimum Dynamical system Convex programming Current control Model predictive control Control constraint Linear time invariant system Discrete time Potential method State constraint |
| Language | English |
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| SubjectTerms | Algorithms Applied sciences Computer science; control theory; systems Control theory. Systems Dynamic programming Dynamical systems Dynamics Exact sciences and technology Explicit Model Predictive Control Mathematical models Model Predictive Control Multi-parametric control Multi-parametric programming Optimal control Optimization Predictive control Programming |
| Title | Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming |
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