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Gradient-Based Parameter Identification Algorithms for Observer Canonical State Space Systems Using State Estimates

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Abstract

This paper considers the parameter identification problem of the state space observer canonical model for linear stochastic systems, and proposes a Kalman filter-based gradient iterative algorithm and an observer-based multi-innovation stochastic gradient algorithm. The fundamental idea is to replace the unmeasurable states in the information vector with the estimated states and to compute the states of the systems through the Kalman filter or the state observer using the previous parameter estimates. Examples are provided to confirm the effectiveness of the proposed algorithms.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61273194) and the PAPD of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, People’s Republic of China

    Xingyun Ma & Feng Ding

  2. Control Science and Engineering Research Center, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Feng Ding

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  1. Xingyun Ma
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  2. Feng Ding
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Correspondence to Feng Ding.

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Ma, X., Ding, F. Gradient-Based Parameter Identification Algorithms for Observer Canonical State Space Systems Using State Estimates . Circuits Syst Signal Process 34, 1697–1709 (2015). https://doi.org/10.1007/s00034-014-9911-5

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  • DOI: https://doi.org/10.1007/s00034-014-9911-5

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