Data-Driven Predictive Control for Linear Parameter-Varying Systems

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