Proportional-integral control for synchronization of complex dynamical networks under dynamic event-triggered mechanism

被引：0
作者：
Suo J. [1 ,2 ]
Shi M. [1 ,2 ]
Li Y. [1 ,2 ]
Yang Y. [1 ,2 ]
机构：
[1] College of Information Science and Technology, Donghua University, Shanghai
[2] Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai
来源：
Journal of the Franklin Institute | 2023年 / 360卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Complex networks - Continuous time systems - Controllers - Dynamics - Two term control systems;
D O I：
10.1016/j.jfranklin.2022.09.048
中图分类号：
学科分类号：
摘要：
In this paper, the exponential synchronization problem is investigated for a class of continuous-time complex dynamical networks (CDNs) with proportional-integral control strategy and dynamic event-triggered mechanism (DETM). To reduce communication overhead, a novel DETM is proposed to decide whether a certain control signal generated by proportional-integral controller should be transmitted or not. The dynamics of each network node is analyzed in conjunction with the proposed proportional-integral strategy under the DETM, and then a sufficient condition for achieving exponential synchronization of CDNs is provided. The validity of the DETM is further verified by the exclusion of the Zeno behavior. The gain matrices of the controller and the parameters of the DETM are jointly designed. The effectiveness of the proportional-integral control strategy under the DETM is demonstrated by a numerical example. © 2022 The Franklin Institute
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页码：1436 / 1453
页数：17
相关论文
共 53 条
[1]  
Zhang Y., Gu B., Error-resilient coding by convolutional neural networks for underwater video transmission, J. Frankl. Inst., 358, 17, pp. 9307-9324, (2021)
[2]  
Boccaletti S., Latora V., Moreno Y., Chavez M., Hwang D.-U., Complex networks: structure and dynamics, Phys. Rep., 424, 4-5, pp. 175-308, (2006)
[3]  
Liu Y., Liu W., Obaid M.A., Abbas I.A., Exponential stability of Markovian jumping Cohen-Grossberg neural networks with mixed mode-dependent time-delays, Neurocomputing, 177, pp. 409-415, (2016)
[4]  
Zanin M., Lillo F., Modelling the air transport with complex networks: a short review, Eur. Phys. J., 215, 1, pp. 5-21, (2013)
[5]  
Zou L., Wang Z., Hu J., Dong H., Partial-node-based state estimation for delayed complex networks under intermittent measurement outliers: a multiple-order-holder approach, IEEE Trans. Neural Netw. Learn. Syst., (2022)
[6]  
Ma L., Wang Z., Chen Y., Yi X., Probability-guaranteed distributed filtering for nonlinear systems with innovation constraints over sensor networks, IEEE Trans. Control Netw. Syst., 8, 2, pp. 951-963, (2021)
[7]  
Sheng L., Niu Y., Zou L., Liu Y., Alsaadi F.E., Finite-horizon state estimation for time-varying complex networks with random coupling strengths under round-robin protocol, J. Frankl. Inst., 355, 15, pp. 7417-7442, (2018)
[8]  
Hu J., Wang Z., Liu G.-P., Delay compensation-based state estimation for time-varying complex networks with incomplete observations and dynamical bias, IEEE Trans. Cybern., (2021)
[9]  
Hou N., Dong H., Wang Z., Liu H., A partial-node-based approach to state estimation for complex networks with sensor saturations under random access protocol, IEEE Trans. Neural Netw. Learn. Syst., 32, 11, pp. 5167-5178, (2021)
[10]  
Hu J., Jia C., Liu H., Yi X., Liu Y., A survey on state estimation of complex dynamical networks, Int. J. Syst. Sci., 52, 16, pp. 3351-3367, (2021)
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