Active Learning for Linear Parameter-Varying System Identification⁎This work was supported by Toyota Motor Corporation, Japan. The first author is also supported by the Elizabeth & Vernon Puzey scholarship. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Chin, Robert; Maass, Alejandro I.; Ulapane, Nalika; Manzie, Chris; Shames, Iman; Nešić, Dragan; Rowe, Jonathan E.; Nakada, Hayato
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 989
• Page End: 994
• Publication Date: 2020
• Subjects: Machine learning -- System identification -- Parameter estimation -- Uncertainty -- Diesel engines; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1274
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Active 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.