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Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data

Author

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  • Feng Ding

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
    College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
    School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

  • Jian Pan

    (School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China)

  • Ahmed Alsaedi

    (Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Tasawar Hayat

    (Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

It is well-known that mathematical models are the basis for system analysis and controller design. This paper considers the parameter identification problems of stochastic systems by the controlled autoregressive model. A gradient-based iterative algorithm is derived from observation data by using the gradient search. By using the multi-innovation identification theory, we propose a multi-innovation gradient-based iterative algorithm to improve the performance of the algorithm. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithms.

Suggested Citation

  • Feng Ding & Jian Pan & Ahmed Alsaedi & Tasawar Hayat, 2019. "Gradient-Based Iterative Parameter Estimation Algorithms for Dynamical Systems from Observation Data," Mathematics, MDPI, vol. 7(5), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:428-:d:230898
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    References listed on IDEAS

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