Title Online learning robust MPC: An exploration-exploitation approach
Authors MANZANO CRESPO, JOSÉ MARÍA, Calliess, J. , Muñoz de la Peña, D. , Limón, D.
External publication Si
Scope Conference Paper
Nature Científica
SJR Quartile 3
SJR Impact 0.308
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105073164&doi=10.1016%2fj.ifacol.2020.12.1210&partnerID=40&md5=81fe4f2486167010b1266d004cfca588
Publication date 01/01/2020
ISI 000652593000154
Scopus Id 2-s2.0-85105073164
DOI 10.1016/j.ifacol.2020.12.1210
Abstract 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. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license
Keywords E-learning; Feedback; Online systems; Predictive control systems; Sampled data control systems; Target tracking; Exploration/exploitation; Learning control; Non linear control; Online learning; Output-feedback; Predictive control; Predictive controller; Robust stability; Sampled data systems; Targets tracking; Controllers
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