Event-triggered ISS-modular neural network control for containment maneuvering of nonlinear strict-feedback multi-agent systems

2020 ◽  
Vol 377 ◽  
pp. 314-324 ◽  
Author(s):  
Yibo Zhang ◽  
Dan Wang ◽  
Zhouhua Peng ◽  
Tieshan Li ◽  
Lu Liu
2021 ◽  
pp. 107754632110368
Author(s):  
Tao Chen ◽  
Jiaxin Yuan ◽  
Hui Yang

This article investigates the consensus problem for a class of fractional-order multi-agent systems with input delay. Each follower is modeled as a system with input delay and nonlinear dynamics. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, dynamic surface control technology is introduced into an adaptive neural network backstepping controller. A dynamic event-triggered scheme without Zeno behavior is considered, which can reduce the utilization of communication resources. The sliding mode control technology is introduced to enhance robustness. The Pade delay approximation method is extended to fractional-order systems, which converts the original systems into systems without input delay. The stability of systems is ensured by the constructed Lyapunov functions. Examples and simulation results show that the consensus tracking errors can quickly converge and all the followers can synchronize to the leader by the proposed method.


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