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Prescribed performance synchronization for uncertain chaotic systems with input saturation based on neural networks

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Abstract

In this paper, a prescribed performance adaptive neural network synchronization is investigated for a class of unknown chaotic systems in the presence of input saturation and external unknown disturbances. A prescribed performance function is employed to transform the constraint problem of chaotic synchronization control error into the problem of guaranteeing the boundedness of the transformed error. By introducing the Gaussian error function, the input saturation is handled. A neural network-based synchronization control scheme is then developed. Under the developed synchronization control scheme, the synchronization of uncertain chaotic systems is achieved with different initial conditions. Numerical simulation results further demonstrate the effectiveness of the proposed synchronization control scheme for unknown chaotic systems subject to external unknown disturbances and input saturation.

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Acknowledgements

This research is supported by National Nature Science Foundation of China (No. 61573184), 333 Talents Project in Jiangsu Province (No. BRA2015359), the Six Talents Peak Project of Jiangsu Province (No. 2012-XXRJ-010), the Fundamental Research Funds for the Central Universities (No. NE2016101) and Jiangsu Innovation Program for Graduate Education (No. KYLX16_0375).

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  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, Jiangsu, China

    Shuyi Shao, Mou Chen & Xiaohui Yan

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  1. Shuyi Shao
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  2. Mou Chen
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  3. Xiaohui Yan
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Correspondence to Mou Chen.

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Shao, S., Chen, M. & Yan, X. Prescribed performance synchronization for uncertain chaotic systems with input saturation based on neural networks. Neural Comput & Applic 29, 1349–1361 (2018). https://doi.org/10.1007/s00521-016-2629-5

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