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

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

author list (cited authors)

• Liang, H., Liu, G., Zhang, H., & Huang, T.

citation count

• 284

complete list of authors

• Liang, Hongjing||Liu, Guangliang||Zhang, Huaguang||Huang, Tingwen

publication date

• May 2021

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• n2162-237XISSN  Journal

keywords

• Adaptive Event-triggered Control
• Control Systems
• Multi-agent Systems
• Neural Networks (nns)
• Nonaffine Multiagent Systems
• Protocols
• Robustness
• Uncertainty
• Unmodeled Dynamics
• Vehicle Dynamics

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2020.3003950

start page

• 2239

end page

• 2250

volume

• 32

issue

• 5

URL

• http://dx.doi.org/10.1109/tnnls.2020.3003950