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Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems

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Abstract

In this paper, the adaptive neural control is proposed for a class of single-input-single-output nonlinear systems with state delay and input saturation. An intercepted adaptation approach is designed to attenuate the effect caused by the input saturation based on a constructed auxiliary system, and radial basis function neural networks are used in the online learning of unknown dynamics. Lyapunov–Krasovskii function is introduced to deal with the state delay. The proposed control scheme can guarantee semi-globally uniformly boundedness of the closed-loop system as rigorously proved by Lyapunov stability theorem. The ultimate and transient tracking errors will be confined in compact regions. The diameters of these regions can be adjusted to be arbitrarily small by tuning proper design parameters. Illustrative examples are used to demonstrate the effectiveness of the proposed control method.

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Acknowledgments

This work is supported jointly by the Fundamental Research Funds for Central Universities (No. 2013JBZ007), National Natural Science Foundation of China (No. 61233001, No. 61322307), Beijing Jiaotong University Research Program (No. RCS2012ZZ003).

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Authors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Shigen Gao, Bin Ning & Hairong Dong

Authors
  1. Shigen Gao
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  2. Bin Ning
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  3. Hairong Dong
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Correspondence to Hairong Dong.

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Gao, S., Ning, B. & Dong, H. Adaptive neural control with intercepted adaptation for time-delay saturated nonlinear systems. Neural Comput & Applic 26, 1849–1857 (2015). https://doi.org/10.1007/s00521-015-1855-6

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  • DOI: https://doi.org/10.1007/s00521-015-1855-6

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