Adaptive neural network dynamic event-triggered consensus control for nonlinear multi-agent systems subject to sensor deception attacks and actuator faults

• Published in: Nonlinear dynamics Vol. 112; no. 22; pp. 20019 - 20034
• Main Authors: Xiao, Junwen, Liu, Yongchao
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2024; Springer Nature B.V
• Subjects: 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; Sensor deception attacks; Neural network; Single parameter learning; Actuator faults; Multi-agent systems; Dynamic event-triggered
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-024-10116-w

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.