Artículo
State-Space Kriging: A Data-Driven Method to Forecast Nonlinear Dynamical Systems
Autor/es | Carnerero Panduro, Alfonso Daniel
Rodríguez Ramírez, Daniel Alamo, Teodoro |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2022-01 |
Fecha de depósito | 2024-04-23 |
Publicado en |
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Resumen | This letter presents a new method for modeling dynamical systems. The method uses historical data of the outputs to predict the evolution of the system. The proposed method is based on Direct Weight Optimization and the ... This letter presents a new method for modeling dynamical systems. The method uses historical data of the outputs to predict the evolution of the system. The proposed method is based on Direct Weight Optimization and the Kriging method. These data-based methods provide predictions as linear combinations of past outputs after solving a quadratic optimization problem. We introduce a novel methodology that we named state-space Kriging , which models the time evolution of the weighting parameters using a state-space formalism. In this way, the potential of Kriging, along with classical estimation methods, as the Kalman filter, can be leveraged to forecast the output of a nonlinear dynamical system. The optimization problems involved are easy to solve, and analytical solutions are provided. Some numerical examples and comparisons are provided to demonstrate the effectiveness of our proposal. |
Agencias financiadoras | Agencia Estatal de Investigación. España European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) Junta de Andalucía |
Identificador del proyecto | PID2019-106212RB-C41/AEI/10.13039/501100011033
P20_00546 |
Cita | Carnerero, A.D., Ramírez, D.R. y Alamo, T. (2022). State-Space Kriging: A Data-Driven Method to Forecast Nonlinear Dynamical Systems. IEEE Control Systems Letters, 6, 2258-2263. https://doi.org/10.1109/LCSYS.2021.3140167. |
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