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Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking

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Abstract

In this paper, finite-time Zhang neural networks (ZNNs) are designed to solve time-varying quadratic program (QP) problems and applied to robot tracking. Firstly, finite-time criteria and upper bounds of the convergent time are reviewed. Secondly, finite-time ZNNs with two tunable activation functions are proposed and applied to solve the time-varying QP problems. Finite-time convergent theorems of the proposed neural networks are presented and proved. The upper bounds of the convergent time are estimated less conservatively. The proposed neural networks also have superior robustness performance against perturbation with large implementation errors. Thirdly, feasibility and superiority of our method are shown by numerical simulations. At last, the proposed neural networks are applied to robot tracking. Simulation results also show the effectiveness of the proposed methods.

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Acknowledgments

This work was supported by the National Science Foundation of China (Nos. 61374028, 61374171, 51177088, 61273183, 61304162), the Grant National Science Foundation of Hubei Provincial (2013CFA050), and the Graduate Scientific Research Foundation of China Three Gorges University (2014PY064, 2014PY069).

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Authors and Affiliations

  1. College of science, China Three Gorges University, Yichang, 443002, China

    Peng Miao

  2. Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang, 443002, China

    Yanjun Shen & Yuehua Huang

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yan-Wu Wang

Authors
  1. Peng Miao
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  2. Yanjun Shen
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  3. Yuehua Huang
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  4. Yan-Wu Wang
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Correspondence to Yanjun Shen.

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Miao, P., Shen, Y., Huang, Y. et al. Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking. Neural Comput & Applic 26, 693–703 (2015). https://doi.org/10.1007/s00521-014-1744-4

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  • DOI: https://doi.org/10.1007/s00521-014-1744-4

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