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Model Predictive Control Design for Linear Parameter Varying Systems: A Survey

Abstract : Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods.
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https://hal.archives-ouvertes.fr/hal-02611783
Contributor : Marcelo Menezes Morato <>
Submitted on : Thursday, May 28, 2020 - 3:18:34 PM
Last modification on : Thursday, July 9, 2020 - 5:02:06 PM

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Marcelo Menezes Morato, Julio Normey-Rico, Olivier Sename. Model Predictive Control Design for Linear Parameter Varying Systems: A Survey. Annual Reviews in Control, Elsevier, 2020, ⟨10.1016/j.arcontrol.2020.04.016⟩. ⟨hal-02611783⟩

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