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A Linear Parameter-Varying Approach to Data Predictive Control
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DOI：10.1109/TAC.2025.3626955.png)
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Abstract
En 中文
By means of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">linear parameter-varying</i> (LPV) Fundamental Lemma, we derive novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-driven predictive control</i> (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.
Keywords:
Behavioral systems
data-driven control
linear parameter-varying (LPV) systems
predictive control
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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