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

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• 作者：Peng Miao (1)
  Yanjun Shen (2)
  Yuehua Huang (2)
  Yan-Wu Wang (3)
  
  1. College of science ; China Three Gorges University ; Yichang ; 443002 ; China
  2. Hubei Provincial Collaborative Innovation Center for New Energy Microgrid ; China Three Gorges University ; Yichang ; 443002 ; China
  3. School of Automation ; Huazhong University of Science and Technology ; Wuhan ; 430074 ; China
  
• 关键词：Time ; varying QP problems ; Finite ; time ZNN ; Tunable activation function ; Upper bound of convergent time ; Robot tracking
• 刊名：Neural Computing & Applications
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：26
• 期：3
• 页码：693-703
• 全文大小：1,078 KB
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• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

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.