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Article Dans Une Revue International Journal of Robust and Nonlinear Control Année : 2021

An Input-to-State Stable Model Predictive Control Framework for Lipschitz Nonlinear Parameter Varying Systems

Résumé

This paper develops a state-feedback Model Predictive Control (MPC) framework for Nonlinear Parameter Varying (NLPV) systems with explicit Lipschitz nonlinearities along the input trajectory. Considering a guess for the evolution of the scheduling parameters along the prediction horizon, the proposed optimization procedure for the MPC design includes a terminal stage cost, a contracting terminal region constraint and nominal predictions scheduled with respect to this guess. The terminal region is taken so that it is monotonically decreasing to a predetermined set, which guarantees recursive feasibility of the algorithm with respect to a bound on the admissible uncertainties (introduced from the scheduling parameter guess). The terminal set is a scheduled robust control invariant set for Lipschitz nonlinear systems, computed through some proposed LMIs. The inclusion of the terminal ingredient also serves to demonstrate Input-to-State Quadratic Stability. This paper ends with a successful numerical simulation example of the technique applied to the control of a Semi-Active automotive suspension system equipped with Electro-Rheological dampers. Comparisons are given with respect to open-loop behaviour and to a Robust LQR controller.
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Dates et versions

hal-02927449 , version 1 (01-09-2020)

Identifiants

Citer

Marcelo Menezes Morato, Julio E Normey-Rico, Olivier Sename. An Input-to-State Stable Model Predictive Control Framework for Lipschitz Nonlinear Parameter Varying Systems. International Journal of Robust and Nonlinear Control, 2021, 31 (7), pp.8239-8272. ⟨10.1002/rnc.5243⟩. ⟨hal-02927449⟩
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