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Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control |
Yangui Xi(喜彦贵), Yongguang Yu(于永光), Shuo Zhang(张硕), Xudong Hai(海旭东) |
Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China |
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Abstract The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method.
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Received: 05 May 2017
Revised: 25 September 2017
Accepted manuscript online:
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PACS:
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02.30.Yy
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(Control theory)
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Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11371049 and 61772063) and the Fundamental Research Funds for the Central Universities, China (Grant No. 2016JBM070). |
Corresponding Authors:
Yongguang Yu
E-mail: ygyu@bjtu.edu.cn
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Cite this article:
Yangui Xi(喜彦贵), Yongguang Yu(于永光), Shuo Zhang(张硕), Xudong Hai(海旭东) Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control 2018 Chin. Phys. B 27 010202
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