caa a22 4500 606159541 CHVBK 20210128100625.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1744-4 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1744-4 Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking [Elektronische Daten] [Peng Miao, Yanjun Shen, Yuehua Huang, Yan-Wu Wang] 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. The Natural Computing Applications Forum, 2014 Time-varying QP problems nationallicence Finite-time ZNN nationallicence Tunable activation function nationallicence Upper bound of convergent time nationallicence Robot tracking nationallicence Miao Peng College of science, China Three Gorges University, 443002, Yichang, China aut Shen Yanjun Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, 443002, Yichang, China aut Huang Yuehua Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, 443002, Yichang, China aut Wang Yan-Wu School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China aut Neural Computing and Applications Springer London 26/3(2015-04-01), 693-703 0941-0643 26:3<693 2015 26 521 https://doi.org/10.1007/s00521-014-1744-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1744-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Miao Peng College of science, China Three Gorges University, 443002, Yichang, China aut NATIONALLICENCE 700 1- Shen Yanjun Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, 443002, Yichang, China aut NATIONALLICENCE 700 1- Huang Yuehua Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, 443002, Yichang, China aut NATIONALLICENCE 700 1- Wang Yan-Wu School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 693-703 0941-0643 26:3<693 2015 26 521