Ponencia
Online learning robust MPC: An exploration-exploitation approach
Autor/es | Manzano, J.M.
Calliess, Jan Peter Muñoz de la Peña Sequedo, David Limón Marruedo, Daniel |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2020 |
Fecha de depósito | 2021-09-06 |
Publicado en |
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ISBN/ISSN | 2405-8963 |
Resumen | This paper presents a predictive controller whose model is based on input-output data of the nonlinear system to be controlled. It uses a Lipschitz interpolation technique in which new data may be included in the database ... This paper presents a predictive controller whose model is based on input-output data of the nonlinear system to be controlled. It uses a Lipschitz interpolation technique in which new data may be included in the database in real time, so the controller improves the system model online. An exploration and exploitation policy is proposed, allowing the controller to robustly and cautiously steer the system to the best reachable reference, even if the model lacks data in such region. The conditions needed to ensure recursive feasibility in the presence of output and input constraints and in spite of the uncertainties are given. The results are illustrated in a simulated case study. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | DPI2016-76493-C3-1-R |
Cita | Manzano, J.M., Calliess, J.P., Muñoz de la Peña Sequedo, D. y Limón Marruedo, D. (2020). Online learning robust MPC: An exploration-exploitation approach. En 21st IFAC World Congress 2020, vol. 53, Issue 2, Article number 145388, (5292-5297), Berlín: Elsevier B.V.; IFAC-PapersOnLine. |
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