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Performance Analysis of the Auxiliary Model-Based Stochastic Gradient Parameter Estimation Algorithm for State-Space Systems with One-Step State Delay

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Abstract

How to use the observation data to build the mathematical models of time-delay systems and how to estimate the parameters of the obtained models are important for studying the laws of motion of systems. This paper presents an auxiliary model-based stochastic gradient parameter estimation algorithm and studies its convergence for the input–output representation for state-space systems with one-step delays, by means of the auxiliary model identification idea. The simulation results indicate that the proposed algorithm can effectively estimate the parameters of the systems.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 60973043), the Natural Science Foundation of Jiangsu Province (China) and the 111 Project (B12018).

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, P.R. China

    Feng Ding

  2. Control Science and Engineering Research Center, Jiangnan University, Wuxi, 214122, P.R. China

    Feng Ding & Ya Gu

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  1. Feng Ding
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Correspondence to Feng Ding.

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Ding, F., Gu, Y. Performance Analysis of the Auxiliary Model-Based Stochastic Gradient Parameter Estimation Algorithm for State-Space Systems with One-Step State Delay. Circuits Syst Signal Process 32, 585–599 (2013). https://doi.org/10.1007/s00034-012-9463-5

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