Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Feedforward controllers typically rely on accurately identified inverse models of the system dynamics to achieve high reference tracking performance. However, the impact of the (inverse) model identification error on the resulting tracking error is only analyzed a posteriori in experiments. Therefore, in this work, we develop an approach to feedforward control design that aims at minimizing the tracking error a priori. To achieve this, we present a model of the system in a lifted space of trajectories, based on which we derive an upperbound on the reference tracking performance. Minimization of this bound yields a feedforward control-oriented system identification cost function, and a finite-horizon optimization to compute the feedforward control signal. The nonlinear feedforward control design method is validated using physics-guided neural networks on a nonlinear, nonminimum phase mechatronic example, where it outperforms linear ILC.
Originele taal-2Engels
Titel2023 IEEE 62nd Conference on Decision and Control, CDC 2023
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's4530-4535
Aantal pagina's6
ISBN van elektronische versie979-8-3503-0124-3
DOI's
StatusGepubliceerd - 19 jan. 2024
Evenement2023 62nd IEEE Conference on Decision and Control (CDC) - Singapore, Singapore, Singapore, Singapore
Duur: 13 dec. 202315 dec. 2023
Congresnummer: 62

Congres

Congres2023 62nd IEEE Conference on Decision and Control (CDC)
Verkorte titelCDC 2023
Land/RegioSingapore
StadSingapore
Periode13/12/2315/12/23

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