Continuous-Time Local Model Network for the Boost-Pressure Dynamics of a Turbocharger

Abstract : In this paper we consider continuous-time local model networks (LMN) to model dynamical processes with strong nonlinearities. The local model approach allows for simple black-box identification procedures using experimental data. Using the LoLiMoT algorithm the number of models can be significantly reduced and may yield insights into the nonlinearities driving the process. We propose a variation of the LoLiMoT algorithm that partitions the operating range in a more efficient manner and proves particular suited for heterogenous nonlinearities.
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Christian Pötzsche; Clemens Heuberger; Barbara Kaltenbacher; Franz Rendl. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. Springer Berlin Heidelberg, IFIP Advances in Information and Communication Technology, AICT-443, pp.348-358, 2014, System Modeling and Optimization. 〈10.1007/978-3-662-45504-3_34〉
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Christoph Weise, Kai Wulff, Marc-Hinrik Höper, Romain Hurtado. Continuous-Time Local Model Network for the Boost-Pressure Dynamics of a Turbocharger. Christian Pötzsche; Clemens Heuberger; Barbara Kaltenbacher; Franz Rendl. 26th Conference on System Modeling and Optimization (CSMO), Sep 2013, Klagenfurt, Austria. Springer Berlin Heidelberg, IFIP Advances in Information and Communication Technology, AICT-443, pp.348-358, 2014, System Modeling and Optimization. 〈10.1007/978-3-662-45504-3_34〉. 〈hal-01286445〉

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