Reliable stability and stabilizability for complex-valued memristive neural networks with actuator failures and aperiodic event-triggered sampled-data control |
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| Institution: | 1. Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China;2. Department of Mathematics, Yangzhou University, Yangzhou 225002, China;3. Communication Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China;2. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea;3. Data Recovery Key Laboratory of Sichuan Province, and Numerical Simulation Key Laboratory of Sichuan Province, College of Mathematics and Information Science, Neijiang Normal University, Neijiang, Sichuan 641100, PR China;4. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;1. Institute of Information and Control, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, PR China;2. Faculty of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, PR China;3. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China |
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| Abstract: | This study addresses the stability and stabilizability problems for complex-valued memristive neural networks (CVMNNs) with actuator failures via reliable aperiodic event-triggered sampled-data control. Different from the traditional control methods with time-triggered mechanism, an aperiodic event-triggered sampled-data control scheme is first proposed for CVMNNs. Taking the influence of actuator failures into account, a reliable controller is designed. In comparison with the existing control approaches, the one here is not only more applicable but effective to save the communication resources for CVMNNs. Then, a new Lyapunov–Krasovskii functional (LKF) is introduced, which can fully capture the information of sampling and complex-valued activation functions. Based on the LKF and some new estimation techniques, novel stability and stabilizability criteria are established, and the desired reliable aperiodic event-triggered sampled-data controller gains are obtained simultaneously. Finally, numerical simulations are provided to verify the effectiveness of the obtained theoretical results. |
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| Keywords: | Reliable control Aperiodic event-triggered sampled-data control Stability Stabilizability Complex-valued memristive neural networks (CVMNNs) Actuator failures |
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