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Event-Triggered-Based Distributed Consensus Tracking for Nonlinear Multiagent Systems With Quantization
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AI
DOI：10.1109/TNNLS.2022.3183639.png)
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Abstract
En 中文
In this article, an observer-based adaptive neural network (NN) event-triggered distributed consensus tracking problem is investigated for nonlinear multiagent systems with quantization. In the first place, the limited capacity of the communication channel between agents is considered. The event-trigger mechanism and dynamic uniform quantizers are set up to reduce information transmission. The next NN is utilized to handle the unknown nonlinear functions. Finally, in order to estimate the unmeasurable states, an NN-based state observer is designed for each agent by using a dynamic gain function. To settle the difficulty caused by the coupling effects of event-triggered conditions and the scaling function in dynamic uniform quantizers and observers, a distributed control protocol with estimated information of its neighbors is designed, which ensures distributed consensus tracking of the nonlinear multiagent systems without incurring the Zeno behavior. The effectiveness of the control protocol is illustrated by a simulation example.
Keywords:
Artificial neural networks
Multi-agent systems
Quantization (signal)
Observers
Protocols
Topology
Nonlinear dynamical systems
Distributed consensus tracking
dynamic gain function
dynamic uniform quantizer
event-trigger mechanism
neural network (NN)
nonlinear multiagent systems
Journal
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8.9
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7.6K
Citations:
7.2W
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