Effects of impulse on prescribed-time synchronization of switching complex networks.

Journal: Neural networks : the official journal of the International Neural Network Society
Published Date:

Abstract

The specified convergence time, designated by the user, is highly attractive for many high-demand applications such as industrial robot control, missile guidance, and autonomous vehicles. For the application of neural networks in the field of secure communication and power systems, the importance of prescribed-time synchronization(PTs) and stable performance of the system is more prominent. This paper introduces a prescribed-time controller without the fractional power function and sign function, which can reach synchronization at a prescribed time and greatly reduce the chattering phenomenon of neural networks. Additionally, by constructing synchronizing/desynchronizing impulse sequences, the PTs of switching complex networks(SCN) is achieved with impulse effects, where the time sequences of switching and impulse occurrences in the networks are constrained by the average dwell time. This approach effectively reduces the impact of frequent mode switching on network synchronization, and the synchronization time can be flexibly adjusted within any physically allowable range to accommodate different application requirements. Finally, the effectiveness of the proposed control strategy is demonstrated by two examples.

Authors

  • Qian Tang
    Department of Radiology, Minda Hospital, Hubei Minzu University, Enshi, People's Republic of China.
  • Shaocheng Qu
    College of Physical Science and Technology, Central China Normal University, Wuhan, 430079, China. Electronic address: qushaocheng@mail.ccnu.edu.cn.
  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Zhengwen Tu
    Department of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing 210996, Jiangsu, China; School of Mathematics and Statistics, and Key Laboratory for Nonlinear Science and System Structure, Chongqing Three Gorges University, Wanzhou 404100, Chongqing, China.
  • Yuting Cao
    School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.