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

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) embeddin...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2024-10, Vol.35 (5), p.802-814
Main Authors: Morato, Marcelo Menezes, Normey-Rico, Julio Elias, Sename, Olivier
Format: Article
Language:English
Subjects:
Algorithms
Automatic
Closed loops
Control
Control and Systems Theory
Control systems
Electrical Engineering
Engineering
Engineering Sciences
Extrapolation
Mechatronics
Nonlinear control
Nonlinear systems
Parameters
Predictive control
Robotics
Robotics and Automation
Trajectory measurement
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Summary: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.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-024-01115-8