Neural network adaptive consensus control for nonlinear multi-agent systems encountered sensor attacks

AbstractThis paper investigates the neural network (NN) adaptive consensus output-feedback control problem for a class of nonlinear multi-agent systems (MASs) encountered sensor attacks. To overcome the impact of unknown sensor attacks, a NN estimation algorithm is adopted to estimate the unknown sensor attacks. Subsequently, a novel NN observer is established to estimate the states of encountered sensor attacks. Consequently, under the framework of backstepping control design, an adaptive NN consensus control method is proposed. By using the Lyapunov stability theory, the proposed consensus control method can not only ensure that all the signals of controlled MASs remain bounded, but also make all followers maintain consensus with the trajectory of the leader. Simulation results and comparative results illustrate the effectiveness of the proposed consensus control scheme.Keywords: Nonlinear multi-agent systemsneural networksensor attacksadaptive consensus control Data availability statementData sharing is not applicable to this article as no new data were created or analysed in this study.Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.Additional informationFundingThis work is supported by National Natural Science Foundation (NNSF) of China [grant numbers 62173172 and U22A2043.Notes on contributorsLexin ChenLexin Chen received the B.S. degree in Mathematics and Applied Mathematics from Xi'an Technological University, Xi'an, China, in 2021. She is working towards the M.E. degree in Applied Mathematics from Liaoning University of Technology, Jinzhou, China. Her research interests include adaptive control, cyber-attacks, fuzzy control and neural networks control, and the security of nonlinear cyber-physical systems.Yongming LiYongming Li (SM'16) received the B.S. degree and the M.S. degree in Applied Mathematics from Liaoning University of Technology, Jinzhou, China, in 2004 and 2007, respectively. He received the Ph. D degree in Transportation Information Engineering & Control from Dalian Maritime University, Dalian, China in 2014. He is currently a Professor in the College of Science, Liaoning University of Technology. His current research interests include adaptive control, fuzzy control and neural networks control for nonlinear systems.Shaocheng TongShaocheng Tong (SM'15) received the B.S. degree in Mathematics from Jinzhou Normal College, Jinzhou, China, the M.S. degree in Fuzzy Mathematics from Dalian Marine University, Dalian, China, and the Ph.D. degree in Fuzzy Control from the Northeastern University, Shenyang, China, in 1982, 1988 and 1997, respectively.He is currently a Professor with the College of Science, Liaoning University of Technology, Jinzhou, China. His current research interests include fuzzy and neural networks control, and nonlinear adaptive control.