Neural Network Output-Feedback Distributed Formation Control for NMASs Under Communication Delays and Switching Network
Haodong Zhou, Shaocheng Tong · IEEE Transactions on Artificial Intelligence · 2025
This article studies the neural network (NN) output-feedback distributed formation control problem of nonlinear multiagent systems (NMASs) under communication delays and jointly connected switching network. Since the communication between agents is affected by time-varying delay and some agents cannot access the leader’s information under jointly connected switching network, a communication-delay-related distributed formation observer is designed to estimate the leader’s information and simultaneously mitigate the effects of communication delays. NNs are adopted to identify unknown functions, and a NN state observer is established to reconstruct unmeasurable states. Then, based on the designed distributed formation observer and NN state observer, a NN output-feedback distributed formation control algorithm is proposed by the backstepping control theory. It is proven that the designed communication-delay-related distributed formation observer errors converge to zero exponentially. Meanwhile, the proposed distributed NN formation control approach ensures the NMAS to be stable, and the formation tracking errors converge to a small neighborhood around zero. Finally, we apply the output-feedback distributed formation control scheme to unmanned surface vehicles (USVs), the simulation results verify its effectiveness.