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Adaptive neural control of stochastic nonlinear systems with multiple time-varying delays and input saturation

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Abstract

This paper investigates the problem of adaptive neural control for a class of strict-feedback stochastic nonlinear systems with multiple time-varying delays, which is subject to input saturation. Via the backstepping technique and the minimal learning parameters algorithm, the problem is solved. Based on the Razumikhin lemma and neural networks’ approximation capability, a new adaptive neural control scheme is developed. The proposed control scheme can ensure that the error variables are semi-globally uniformly ultimately bounded in the sense of four-moment, while all the signals in the closed-loop system are bounded in probability. Two simulation examples are provided to demonstrate the effectiveness of the proposed control approach.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful comments that improve the quality of the paper. This work was supported by the National Natural Science Foundation of P.R. China under Grants 61374086, 61174137, 61104064, 61374153, 61203024, 61170054 and the Graduate Innovation and Creativity Foundation of Jiangsu Province under Grant CXZZ13_0209.

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, People’s Republic of China

    Guozeng Cui, Ticao Jiao, Yunliang Wei & Gongfei Song

  2. School of Science, Huzhou Teachers College, Huzhou, 313000, Zhejiang, People’s Republic of China

    Yuming Chu

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  1. Guozeng Cui
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  2. Ticao Jiao
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  3. Yunliang Wei
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  4. Gongfei Song
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  5. Yuming Chu
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Correspondence to Guozeng Cui.

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Cui, G., Jiao, T., Wei, Y. et al. Adaptive neural control of stochastic nonlinear systems with multiple time-varying delays and input saturation. Neural Comput & Applic 25, 779–791 (2014). https://doi.org/10.1007/s00521-014-1548-6

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  • DOI: https://doi.org/10.1007/s00521-014-1548-6

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