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Disturbance attenuation for nonlinear switched descriptor systems based on neural network

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Abstract

In this paper, we address the problem of neural network-based disturbance attenuation for a class of nonlinear switched descriptor systems. An adaptive neural switching control scheme is designed so that such system can asymptotically track the desired reference model and attenuate the external disturbance to a prescribed level. By approximating the unknown nonlinear function vectors based on RBF neural network, we incorporate the network reconstruction error and exterior disturbance into the design framework of adaptive neural switching control strategy. The RBF neural network is used to compensate for the nonlinear uncertainties of switched descriptor systems, and the reconstruction error of RBF neural network is introduced to the adaptive law in order to improve the tracking attenuation quality of overall switched systems. Under the assumptions of regular and impulse free, the designed adaptive neural switching controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched descriptor system. Finally, an example is given to demonstrate effectiveness of the proposed adaptive neural network-based switching control scheme.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive and insightful comments for further improving the quality of this work. This work was partially supported by the Natural Science Foundation of China (Nos. 61263005, 61065010), the Doctor’s foundation of Higher Education under Grant No. 20105201120003 and the Graduate School Innovation Foundation of Guizhou University under Grant No. 2012031.

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Authors and Affiliations

  1. Institute of Intelligent Information Processing, Guizhou University, Guiyang, 550025, Guizhou, People’s Republic of China

    Fei Long, Yang Xiao, Xin Chen & Zhuhong Zhang

  2. College of Computer Science and Information, Guizhou University, Guiyang, 550025, Guizhou, People’s Republic of China

    Fei Long, Yang Xiao & Xin Chen

  3. Institute of System Science and Information Technology, College of Science, Guizhou University, Guiyang, 550025, Guizhou, People’s Republic of China

    Zhuhong Zhang

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  1. Fei Long
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  2. Yang Xiao
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  4. Zhuhong Zhang
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Correspondence to Fei Long.

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Long, F., Xiao, Y., Chen, X. et al. Disturbance attenuation for nonlinear switched descriptor systems based on neural network. Neural Comput & Applic 23, 2211–2219 (2013). https://doi.org/10.1007/s00521-012-1171-3

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  • DOI: https://doi.org/10.1007/s00521-012-1171-3

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