Computationally efficient predictive control based on ANN state-space model

Jan H. Hoekstra, Bence Cseppentő, G.I. Beintema, Maarten Schoukens, Zsolt Kollár, Roland Tóth

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme exploits little of the structural properties or available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN state-space models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing ANN identification methods, such as the SUBNET method that uses a state encoder, can provide efficient implementation of MPCs. The performance of the proposed approach is demonstrated via a simulation study on an unbalanced disc system.
Originele taal-2Engels
Titel2023 62nd IEEE Conference on Decision and Control, CDC 2023
UitgeverijInstitute of Electrical and Electronics Engineers
Aantal pagina's6
ISBN van elektronische versie979-8-3503-0124-3
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


Congres2023 62nd IEEE Conference on Decision and Control (CDC)
Verkorte titelCDC 2023


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