期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 63, 期 11, 页码 6978-6988出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2016.2590379
关键词
Modified Zhang neural network (MZNN); random noise; redundancy resolution; theoretical analyses; time-varying quadratic programming (TVQP)
资金
- National Natural Science Foundation of China [61473323, 61401385]
- Science and Technology Program of Guangzhou, China [2014J4100057]
- Hong Kong Research Grants Council [25214015]
- Hong Kong Polytechnic University [G.61.37.UA7L]
For quadratic programming (QP), it is usually assumed that the solving process is free of measurement noises or that the denoising has been conducted before the computation. However, time is precious for time-varying QP (TVQP) in practice. Preprocessing for denoising may consume extra time, and consequently violates real-time requirements. Therefore, a model with inherent noise tolerance is urgently needed to solve TVQP problems in real time. In this paper, wemake progress along this direction by proposing a modified Zhang neural network (MZNN) model for the solution of TVQP. The original Zhang neural network model and the gradient neural network model are employed for comparisons with the MZNN model. In addition, theoretical analyses show that, without measurement noise, the proposed MZNN model globally converges to the exact real-time solution of the TVQP problem in an exponential manner and that, in the presence of measurement noises, the proposed MZNN model has a satisfactory performance. Finally, two illustrative simulation examples as well as a physical experiment are provided and analyzed to substantiate the efficacy and superiority of the proposed MZNN model for TVQP problem solving.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据