Chin, RobertMaass, Alejandro I.Ulapane, NalikaManzie, ChrisShames, ImanNešic, DraganRowe, Jonathan E.Nakada, Hayato2025-06-102025-06-102405-8963http://www.scopus.com/inward/record.url?scp=85102164283&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733757990Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.This work was supported by Toyota Motor Corporation, Japan. The first author is also supported by the Elizabeth & Vernon Puzey scholarship. The authors would like to thank the engineering staff at Toyota Motor Corporation Higashi-Fuji Technical Centre in Japan for their assistance in running the experiments related to this work.6enPublisher Copyright: Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND licenseDiesel enginesMachine learningParameter estimationSystem identificationUncertainty202010.1016/j.ifacol.2020.12.127485102164283