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Data-Driven Predictive Control for Linear Parameter-Varying Systems⁎This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement nr. 714663), the European Space Agency in the scope of the 'AI4GNC' project with SENER Aeroespacial S.A. (contract nr. 4000133595/20/NL/CRS), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project No. 419290163. Issue 8 (2021)
Record Type:
Journal Article
Title:
Data-Driven Predictive Control for Linear Parameter-Varying Systems⁎This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement nr. 714663), the European Space Agency in the scope of the 'AI4GNC' project with SENER Aeroespacial S.A. (contract nr. 4000133595/20/NL/CRS), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project No. 419290163. Issue 8 (2021)
Main Title:
Data-Driven Predictive Control for Linear Parameter-Varying Systems⁎This work has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement nr. 714663), the European Space Agency in the scope of the 'AI4GNC' project with SENER Aeroespacial S.A. (contract nr. 4000133595/20/NL/CRS), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project No. 419290163.
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.