Adaptive neural network dynamic event-triggered consensus control for nonlinear multi-agent systems subject to sensor deception attacks and actuator faults
This paper presents an adaptive neural network dynamic event-triggered consensus method for nonlinear multi-agent systems (MASs) under sensor deception attacks and actuator faults. A single parameter learning method is integrated into backstepping technique to simplify the design procedure. The neur...
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| Published in | Nonlinear dynamics Vol. 112; no. 22; pp. 20019 - 20034 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Dordrecht
Springer Netherlands
01.11.2024
Springer Nature B.V |
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| Abstract | This paper presents an adaptive neural network dynamic event-triggered consensus method for nonlinear multi-agent systems (MASs) under sensor deception attacks and actuator faults. A single parameter learning method is integrated into backstepping technique to simplify the design procedure. The neural network is utilized to compensate for the unknown dynamics of MASs. The designed controller can withstand sensor deception attacks and accommodate actuator faults. Moreover, the dynamic event-triggered mechanism is designed to conserve communication resources. The designed control law ensures that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method. |
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| AbstractList | This paper presents an adaptive neural network dynamic event-triggered consensus method for nonlinear multi-agent systems (MASs) under sensor deception attacks and actuator faults. A single parameter learning method is integrated into backstepping technique to simplify the design procedure. The neural network is utilized to compensate for the unknown dynamics of MASs. The designed controller can withstand sensor deception attacks and accommodate actuator faults. Moreover, the dynamic event-triggered mechanism is designed to conserve communication resources. The designed control law ensures that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method. |
| Author | Xiao, Junwen Liu, Yongchao |
| Author_xml | – sequence: 1 givenname: Junwen surname: Xiao fullname: Xiao, Junwen organization: School of Automation, Qingdao University, Shandong Key Laboratory of Industrial Control Technology – sequence: 2 givenname: Yongchao surname: Liu fullname: Liu, Yongchao email: sdliuyc@163.com organization: School of Automation, Qingdao University, Shandong Key Laboratory of Industrial Control Technology |
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| Keywords | Sensor deception attacks Neural network Single parameter learning Actuator faults Multi-agent systems Dynamic event-triggered |
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| Snippet | This paper presents an adaptive neural network dynamic event-triggered consensus method for nonlinear multi-agent systems (MASs) under sensor deception attacks... |
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| SubjectTerms | Actuators Adaptive systems Automotive Engineering Classical Mechanics Communication Control Control algorithms Control systems design Control theory Deception Design Dynamical Systems Energy consumption Engineering Euclidean space Fault tolerance Faults Mechanical Engineering Multiagent systems Neural networks Nonlinear control Nonlinear systems Sensors Teaching methods Vibration |
| Title | Adaptive neural network dynamic event-triggered consensus control for nonlinear multi-agent systems subject to sensor deception attacks and actuator faults |
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