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Improved Gravitational Search and Gradient Iterative Identification for Multivariable Hammerstein Time-Delay Systems

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Abstract

Multivariable Hammerstein time-delay (MHTD) systems have been widely used in a variety of complex industrial systems; thus, it is of great significance to identify the parameters of such systems. The MHTD system is difficult to identify due to its inherent complexity. As one of heuristic algorithms, the gravitational search algorithm is suitable for identifying such complex models, but it has the problem of easily falling into local optimum. Therefore, this paper combines the improved chaotic gravitational search algorithm (ICGSA) and gradient iterative (GI) algorithm and proposes an ICGSA–GI algorithm to overcome the shortcomings of the above two algorithms. Then, we use it to identify the unknown parameters of the MHTD systems. Finally, a numerical example and an application case are given for validating the feasibility of the three identification methods. The results demonstrate that the three algorithms can identify the unknown parameters of the MHTD system effectively. In addition, by comparing with ICGSA and GI algorithms, this paper confirms that the ICGSA–GI algorithm behaves better than ICGSA and GI algorithm in identification accuracy, and the convergence speed of the ICGSA–GI algorithm is faster than that of the GI algorithm.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (61973176, 61973178, U2066203) and the Qinglan Project of Jiangsu Province of China.

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  1. School of Electrical Engineering, Nantong University, Nantong, 226019, People’s Republic of China

    Junhong Li, Weicheng Song, Yizhe Jiang & Jie Chu

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  1. Junhong Li
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Li, J., Song, W., Jiang, Y. et al. Improved Gravitational Search and Gradient Iterative Identification for Multivariable Hammerstein Time-Delay Systems. Circuits Syst Signal Process 42, 5161–5186 (2023). https://doi.org/10.1007/s00034-023-02361-y

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