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Neural adaptive control of uncertain chaotic systems with input and output saturation

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Abstract

This paper is concerned with the neural adaptive control design problem of a class of chaotic systems with uncertain dynamics, input and output saturation. To attenuate the effect caused by input and output saturation, a constructed auxiliary system is used to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks are used in online approximation of the uncertain dynamics. Both state feedback and output feedback control laws are designed. In the output feedback situation, a high-order sliding-mode observer is used to estimate the system states. The stability of closed loop system is proved rigorously based on Lyapunov theorem. The effectiveness of the proposed methods is demonstrated by controlling Duffing system and Genesio system.

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Acknowledgments

This work is supported jointly by the National Natural Science Foundation of China (Nos. 61322307 and 61233001), Fundamental Research Funds for Central Universities (No. 2013JBZ007), and 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, Hairong Dong & Bin Ning

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

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Gao, S., Dong, H. & Ning, B. Neural adaptive control of uncertain chaotic systems with input and output saturation. Nonlinear Dyn 80, 375–385 (2015). https://doi.org/10.1007/s11071-014-1875-2

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  • DOI: https://doi.org/10.1007/s11071-014-1875-2

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