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arXiv · 2005.00711

Active Learning for Linear Parameter-Varying System Identification

Robert Chin·Alejandro I. Maass·Nalika Ulapane·Chris Manzie·Iman Shames·Dragan Nešić·Jonathan E. Rowe·Hayato Nakada
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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.

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Robert Chin, Alejandro I. Maass, Nalika Ulapane, Chris Manzie, Iman Shames, Dragan Nešić, Jonathan E. Rowe, Hayato Nakada. 2020-05-02. Active Learning for Linear Parameter-Varying System Identification. https://arxiv.org/abs/2005.00711

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