Work

A Linear Parameter-Varying Approach to Data Predictive Control

Year: 2025

Type: article

Abstract: By means of thelinear parameter-varying(LPV) Fundamental Lemma, we derive noveldata-driven predictive control(DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the application and implementation aspects of the LPV-DPC schemes is given, including application for nonlinear systems and handling noisy data. We compare and demonstrate the performance of the proposed methods in a detailed simulation example involving a nonlinear unbalanced disc system.

Source: IEEE Transactions on Automatic Control

DOI: 10.1109/tac.2025.3626955

Authors: Chris Verhoek, Julian Berberich, Sofie Haesaert, Roland Tóth, Hossam S. Abbas

Institutions: Eindhoven University of Technology, University of Stuttgart, University of Lübeck

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Deutsche Forschungsgemeinschaft, H2020 European Research Council

Awards: G424312806, G434337789, Angewandte Modellprädiktive Regelung für Nichtlineare/Zeitveränderliche Systeme unter Verwendung von LinearParameter Veränderlicher Modelle, Automated Linear Parameter-Varying Modeling and Control Synthesis for Nonlinear Complex Systems