Iterative learning control-based tracking synchronization for linearly coupled reaction-diffusion neural networks with time delay and iteration-varying switching topology |
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| Authors: | Xingyu Zhou Haoping Wang Yang Tian Xisheng Dai |
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| Institution: | 1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;2. School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China;1. College of Science, Hebei Agricultural University, Baoding 071001, China;2. School of Science, Nanjing University of Science and Technology, Nanjing 210094, China;3. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China;4. School of Mathematics and Physics, Anhui Polytechnic University, Wuhu 241000, China;1. School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China;2. School of Mathematics and Physics, Guangxi University for Nationalities, Nanning 530006, China;1. Medical IT Convergence Research Section, Electronics and Telecommunications Research Institute (ETRI), Daegu 42994, Republic of Korea;2. School of Electronic and Electrical Engineering, Kyungpook National University, Daehak-ro 80, Republic of Korea;3. Smart Mobility Research Section, Electronics and Telecommunications Research Institute (ETRI), Daegu, Republic of Korea;4. Cyber Physical Systems & Control Laboratory, School of Electronic and Electrical Engineering, Kyungpook National University, Daehak-ro 80, Republic of Korea;1. Graduate School of Mechanical and Aerospace Engineering, Gyeongsang National University, 501 Jinjudaero, Jinju 52828, Republic of Korea;2. Department of Electrical Engineering, Hanyang University, 222 Wangsimniro, Seoul 04763, Republic of Korea;1. Smart Energy Systems Research Group, School of Engineering, Edith Cowan University, Australia;2. Department of Mechanical Engineering, School of Engineering, The University of Melbourne, Australia |
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| Abstract: | In this paper, the D-type iterative learning control (ILC) protocol based on the local neighbor information is designed to achieve tracking synchronization for linearly coupled reaction-diffusion neural networks in presence of time delay and iteration-varying switching topology under a repetitive environment. Firstly, based on non-collocated sensors and actuators network, the proposed D-type ILC update law can realize tracking synchronization by utilizing output tracking errors. Then, by virtue of the contraction mapping principle, the sufficient convergence conditions of tracking synchronization errors are presented under the fixed commutation topology. Subsequently, the synchronization conclusions are extended to the iteration-varying commutation topology scenario. Finally, two numerical examples are provided to verify the efficacy of the obtained results. |
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