Online learning robust MPC: An exploration-exploitation approach

Autor: Daniel Limon, Jan-Peter Calliess, D. Muñoz de la Peña, J.M. Manzano
Přispěvatelé: Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática, Ministerio de Economía y Competitividad (MINECO). España
Rok vydání: 2020
Předmět:
Zdroj: idUS. Depósito de Investigación de la Universidad de Sevilla
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Popis: Cuenta con otro ed.: IFAC-PapersOnLine Incluída en el vol. 53, Issue 2 Article number 145388 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. Feder (UE) DPI2016-76493-C3-1-R
Databáze: OpenAIRE
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