Nonlinear Data-Driven Control Part II: qLPV Predictive Control with Parameter Extrapolation - Université Grenoble Alpes
Article Dans Une Revue Journal of Control, Automation and Electrical Systems Année : 2024

Nonlinear Data-Driven Control Part II: qLPV Predictive Control with Parameter Extrapolation

Résumé

We present a novel data-driven Model Predictive Control (MPC) algorithm for nonlinear systems. The method is based on recent extensions of behavioural theory and Willem’s Fundamental Lemma for nonlinear systems by the means of adequate Input–Output (IO) quasi-Linear Parameter-Varying (qLPV) embeddings. Thus, the MPC is formulated to ensure regulation and IO constraints satisfaction, based only on measured datasets of sufficient length (and under persistent excitation). The main innovation is to consider the knowledge of the function that maps the qLPV realisation, and apply an extrapolation procedure in order to generate the corresponding future scheduling trajectories, at each sample. Accordingly, we briefly discuss the issues of closed-loop IO stability and recursive feasibility certificates of the method. The algorithm is tested and discussed with the aid of a numerical application.
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Dates et versions

hal-04740398 , version 1 (16-10-2024)

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Citer

Marcelo Menezes Morato, Julio Elias Normey-Rico, Olivier Sename. Nonlinear Data-Driven Control Part II: qLPV Predictive Control with Parameter Extrapolation. Journal of Control, Automation and Electrical Systems, 2024, 35 (5), pp.802-814. ⟨10.1007/s40313-024-01115-8⟩. ⟨hal-04740398⟩
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