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RBF neural networks-based robust adaptive tracking control for switched uncertain nonlinear systems

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Abstract

In this paper, a robust adaptive H∞ control scheme is presented for a class of switched uncertain nonlinear systems. Radical basis function neural networks (RBF NNs) are employed to approximate unknown nonlinear functions and uncertain terms. A robust H∞ controller is designed to enhance robustness due to the existence of the compound disturbance which consists of approximation errors of the neural networks and external disturbance. Adaptive neural updated laws and switching signals are deducted from multiple Lyapunov function approach. It is proved that with the proposed control scheme, the resulting closed-loop switched system is robustly stable and uniformly ultimately bounded (UUB) such that good capabilities of tracking performance is attained and H∞ tracking error performance index is achieved. A practical example shows the effectiveness of the proposed control scheme.

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Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Soochow University, Suzhou, 215021, China

    Lei Yu

  2. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai, 200237, China

    Lei Yu

  3. Henan Provincial Open Laboratory for Control Engineering Key Discipline, Jiaozuo, 454000, China

    Lei Yu

  4. School of Automation, Southeast University, Nanjing, 210096, China

    Shumin Fei & Xun Li

Authors
  1. Lei Yu
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  2. Shumin Fei
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  3. Xun Li
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Corresponding author

Correspondence to Lei Yu.

Additional information

Recommended by Editorial Board member Bin Jiang under the direction of Editor Zengqi Sun.

This work was supported by the National Natural Science Foundation of China (Nos. 60835001, 11072164, 61104068, 61104119); The Foundation of Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, P.R. China (2012ACOCP03); The Foundation of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, China; The Open Laboratory Foundation of Control Engineering Key Discipline of Henan Provincial High Education, China (Grant No.KG2011-02); Application research programs of NanTong City (No. K2010057).

Lei Yu was born in Xuancheng, P. R. China in 1983. He received his M.S. degree in Control Theory and Control Engineering from Hefei University of Technology, China in 2008 and his Ph.D. degree in Automatic Control Theory and Applications from Southeast University in 2011. He is now a lecturer in the School of Mechanical and Electrical Engineering, Soochow University. His research interests are in switched nonlinear systems, robust adaptive control, neural network control.

Shumin Fei was born in 1961. He received his Ph.D. degree from Beihang University, Beijing, P. R. China in 1995. From 1995 to 1997, he did postdoctoral research in the Research Institute of Automation at Southeast University. He is now a professor in the Research Institute of Automation at Southeast University, Nanjing, P. R. China. His research interests include analysis and synthesis of nonlinear systems, robust control, adaptive control and analysis and synthesis of time-delay systems.

Xun Li was born in Nanyang, P. R. China in 1977. He is currently pursuing a Ph.D. degree at College of Automa-tion, Southeast University, Nanjing, P. R. China. The main research interests include nonlinear systems, pattern recognition and artificial intelligence.

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Yu, L., Fei, S. & Li, X. RBF neural networks-based robust adaptive tracking control for switched uncertain nonlinear systems. Int. J. Control Autom. Syst. 10, 437–443 (2012). https://doi.org/10.1007/s12555-012-0224-9

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  • DOI: https://doi.org/10.1007/s12555-012-0224-9

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