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Nonlinear Data-Driven Control Part II: qLPV Predictive Control with Parameter Extrapolation.
- Source :
- Journal of Control, Automation & Electrical Systems; Oct2024, Vol. 35 Issue 5, p802-814, 13p
- Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21953880
- Volume :
- 35
- Issue :
- 5
- Database :
- Supplemental Index
- Journal :
- Journal of Control, Automation & Electrical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 179505224
- Full Text :
- https://doi.org/10.1007/s40313-024-01115-8