Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dy...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-05, Vol.32 (5), p.2239-2250
Main Authors: Liang, Hongjing, Liu, Guangliang, Zhang, Huaguang, Huang, Tingwen
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive event-triggered control
Algorithms
Control systems
Feedback control
Liapunov functions
Multi-agent systems
Multiagent systems
Neural networks
neural networks (NNs)
nonaffine multiagent systems
Nonlinear control
Nonlinear systems
Protocols
Radial basis function
Robustness
Uncertainty
unmodeled dynamics
Vehicle dynamics
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Summary:This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. Radial basis function neural networks are applied to approximate the unknown nonlinear function. A dynamic signal is constructed to deal with the design difficulties in the unmodeled dynamics. Moreover, to reduce the communication burden, we propose an event-triggered strategy with a varying threshold. Based on the Lyapunov function method and adaptive neural control approach, a novel event-triggered control protocol is constructed, which realizes that the outputs of all followers converge to a neighborhood of the leader's output and ensures that all signals are bounded in the closed-loop system. An illustrative simulation example is applied to verify the usefulness of the proposed algorithms.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3003950