Article
Kernel-based State-Space Kriging for Predictive Control
Author/s | Carnerero Panduro, Alfonso Daniel
Rodríguez Ramírez, Daniel Limón Marruedo, Daniel Alamo, Teodoro |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2023-05 |
Deposit Date | 2024-04-23 |
Published in |
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Abstract | In this paper, we extend the State-Space Kriging (SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as ... In this paper, we extend the State-Space Kriging (SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the Kernel-based State-Space Kriging (K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking Nonlinear Model Predictive Control (NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller. |
Funding agencies | Agencia Estatal de Investigación. España European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) Junta de Andalucía |
Project ID. | PID2019-106212RB-C41/AEI/10.13039/501100011033
P20_00546 |
Citation | Carnerero, A.D., Ramírez, D.R., Limón, D. y Alamo, T. (2023). Kernel-based State-Space Kriging for Predictive Control. IEEE/CAA Journal of Automatica Sinica, 10 (5), 1263-1275. https://doi.org/10.1109/JAS.2023.123459. |
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