Implications of Inverse Parametric Optimization in Model Predictive Control

Martin Gulan 1 Ngoc Anh Nguyen 2, 3 Sorin Olaru 4, 3, 2 Pedro Rodriguez-Ayerbe 3, 2 Boris Rohal'-Ilkiv 1
4 DISCO - Dynamical Interconnected Systems in COmplex Environments
L2S - Laboratoire des signaux et systèmes, Inria Saclay - Ile de France, SUPELEC, CNRS - Centre National de la Recherche Scientifique : UMR8506
Abstract : Recently, inverse parametric linear/quadratic programming problem was shown to be solvable via convex liftings approach [13]. This technique turns out to be relevant in explicit model predictive control (MPC) design in terms of reducing the prediction horizon to at most two steps. In view of practical applications, typically leading to problems that are not directly invertible, we show how to adapt the inverse optimality to specific, possibly convexly non-liftable partitions. Case study results moreover indicate that such an extension leads to controllers of lower complexity without loss of optimality. Numerical data are also presented for illustration.
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Submitted on : Thursday, January 21, 2016 - 12:36:09 PM
Last modification on : Wednesday, April 18, 2018 - 10:32:05 AM

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Martin Gulan, Ngoc Anh Nguyen, Sorin Olaru, Pedro Rodriguez-Ayerbe, Boris Rohal'-Ilkiv. Implications of Inverse Parametric Optimization in Model Predictive Control. Developments in Model-Based Optimization and Control , 464, pp.49-70, 2015, ⟨10.1007/978-3-319-26687-9_3 ⟩. ⟨hal-01259959⟩

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