Data-Driven Predictive Control for Linear Parameter-Varying Systems

Abstract

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the IO data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the data-based LPV-DPC scheme, achieves similar performance as LPV model-based predictive control.

Accepted to 4th IFAC Workshop on Linear Parameter-Varying Systems

References in corpus (1)

• LPVcore: MATLAB Toolbox for LPV Modelling, Identification and Control

Cited by in corpus (8)

• An overview of systems-theoretic guarantees in data-driven model predictive control
• On a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control
• Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems
• Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
• Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
• Frequency-Domain Data-Driven Predictive Control
• A Linear Parameter-Varying Approach to Data Predictive Control
• Autonomous path following using data-driven predictive control