Artículo
Robust learning-based MPC for nonlinear constrained systems
Autor/es | Manzano Crespo, José María
Limón Marruedo, Daniel Muñoz de la Peña Sequedo, David Calliess, Jan Peter |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2020-07 |
Fecha de depósito | 2021-05-13 |
Publicado en |
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Premios | Premio Trimestral Publicación Científica Destacada de la US. Escuela Técnica Superior de Ingeniería |
Resumen | This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only ... This paper presents a robust learning-based predictive control strategy for nonlinear systems subject to both input and output constraints, under the assumption that the model function is not known a priori and only input–output data are available. The proposed controller is obtained using a nonparametric machine learning technique to estimate a prediction model. Based on this prediction model, a novel stabilizing robust predictive controller without terminal constraint is proposed. The design procedure is purely based on data and avoids the estimation of any robust invariant set, which is in general a hard task. The resulting controller has been validated in a simulated case study. |
Cita | Manzano, J.M., Limón, D., Muñoz de la Peña, D. y Calliess, J.-P. (2020). Robust learning-based MPC for nonlinear constrained systems. Automatica, 117, Art. number 108948. |
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