@inbook{4ff92b4bb63c4f50bb499d8c9d9ca11d,
title = "Exploring Unknown Plant Configurations under a Multiple Model Adaptive Control Framework",
abstract = "Multiple model adaptive control (MMAC) provides robust control guarantees for a range of plant configurations given a set of models that correspond to known operating conditions (modes). However, these guarantees may not hold in the face of unknown system configurations. This paper focuses on the first step of control under novel modes: the task of novel configuration detection. We analyze a change-point detection strategy for unknown system configurations using input/output data as input. We characterize the performance of the changepoint detection strategy based on whether the performance loss is bounded or unbounded. We experimentally validate our algorithms using a quadcopter trajectory-tracking benchmark, comparing our approach to a Bayesian change-point detection strategy.",
keywords = "changepoint detection, multiple-model adaptive control, plant parameter changes",
author = "Ares-Mili{\'a}n, \{Marlon Jes{\'u}s\} and Gregory Provan and Yves Soh{\`e}ge and Marcos Quinones-Grueiro",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.1415",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "2933--2938",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
address = "Netherlands",
edition = "2",
}