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Quantized neural adaptive finite-time preassigned performance control for interconnected nonlinear systems

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Abstract

In this article, the issue of neural adaptive decentralized finite-time prescribed performance (FTPP) control is investigated for interconnected nonlinear time-delay systems. First, to bypass the potential singularity difficulties, the hyperbolic tangent function and the radial basis function neural networks are integrated to handle the unknown nonlinear items. Then, an adaptive FTPP control strategy is developed, where an improved fractional-order filter is applied to tackle the tremendous “amount of calculation” and eliminate the filter error simultaneously. Furthermore, by considering the impact of bandwidth limitation, an adaptive self-triggered control law is designed, in which the next trigger instant is determined through the current information. Ultimately, it can be demonstrated that the proposed control scheme not only guarantees that all states of the closed-loop system are semi-globally uniformly ultimately bounded, but also that the system output is confined to a small area in finite time. Two simulation examples are carried out to verify the effectiveness and superiority of the proposed method.

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Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61976081 and 62203153, in part by the Natural Science Fund for Excellent Young Scholars of Henan Province under Grant 202300410127, in part by Key Scientific Research Projects of Higher Education Institutions in Henan Province under Grant 22A413001, in part by Top Young Talents in Central Plains under Grant Yuzutong (2021) 44, in part by Technology Innovative Teams in University of Henan Province under Grant 23IRTSTHN012, in part by the Natural Science Fund for Young Scholars of Henan Province under Grant 222300420151, and in part by the Serbian Ministry of Education, Science and Technological Development (No. 451-03-68/2022-14/200108).

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Authors and Affiliations

  1. School of Information Engineering, Henan University of Science and Technology, Kaiyuan Avenu, Luoyang, 471023, Henan, China

    Xiaona Song, Peng Sun & Shuai Song

  2. Department of Automatic Control, Robotics and Fluid Technique, Faculty of Mechanical and Civil Engineering, University of Kragujevac, Kraljevo, 36000, Serbia

    Vladimir Stojanovic

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  1. Xiaona Song
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  2. Peng Sun
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  3. Shuai Song
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  4. Vladimir Stojanovic
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Correspondence to Xiaona Song.

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Song, X., Sun, P., Song, S. et al. Quantized neural adaptive finite-time preassigned performance control for interconnected nonlinear systems. Neural Comput & Applic 35, 15429–15446 (2023). https://doi.org/10.1007/s00521-023-08361-y

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