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• W2772069634 abstract "This paper deals with the identification problem of a linear parameter varying (LPV) model with a noisy (uncertain) scheduling variable. An autoregressive exogenous (ARX) model is used to describe the system behavior and a nonlinear state-space model is used to model dynamics of the noisy scheduling variable. The uncertain scheduling variable is estimated by using a particle smoother, which is based on sequential Monte-Carlo (SMC) method, and all the unknown parameters in the LPV ARX model are identified under the framework of an expectation-maximization (EM) algorithm based on the intput-output data of the system. A numerical example and a mechanical unit are used to validate the proposed approach." @default.
• W2772069634 created "2017-12-22" @default.
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• W2772069634 date "2017-10-01" @default.
• W2772069634 modified "2023-09-22" @default.
• W2772069634 title "EM-based global identification of LPV ARX models with a noisy scheduling variable" @default.
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• W2772069634 doi "https://doi.org/10.1109/iecon.2017.8217202" @default.
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