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Multiple models switching control based on recurrent neural networks

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Abstract

This paper presents a novel approach in designing adaptive controller to improve the transient performance for a class of nonlinear discrete-time systems under different operating modes. The proposed scheme consists of generalized minimum variance (GMV) controllers and a compensating controller. GMV controllers are based on the known nominal linear multiple models, while the compensating controller is based upon a recurrent neural network. The adaptation law of network weight is derived from Lyapunov stability theory. A suitable switching control strategy is applied to choose the best controller by the performance indices at every sampling instant. Simulations are discussed in order to illustrate the merits of the proposed method.

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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 is supported by National Natural Science Foundation of China (60404006, 60574006).

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Authors and Affiliations

  1. Research Institute of Automation, Southeast University, Nanjing, 210096, China

    Jun-Yong Zhai & Shu-Min Fei

  2. Department of Information Technology, Jinling Institute of Technology, Nanjing, 210001, China

    Xiao-Hui Mo

Authors
  1. Jun-Yong Zhai
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  2. Shu-Min Fei
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  3. Xiao-Hui Mo
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Corresponding author

Correspondence to Jun-Yong Zhai.

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Zhai, JY., Fei, SM. & Mo, XH. Multiple models switching control based on recurrent neural networks. Neural Comput & Applic 17, 365–371 (2008). https://doi.org/10.1007/s00521-007-0123-9

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  • DOI: https://doi.org/10.1007/s00521-007-0123-9

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