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

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 5; pp. 2239 - 2250
• Main Authors: Liang, Hongjing, Liu, Guangliang, Zhang, Huaguang, Huang, Tingwen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3003950

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.