Neural Network Control of Distributed Cooperative Formation of Multi-agent System

The paper presented in this article deals with the issue of distributed cooperative formation of multi-agent systems (MASs). It proposes the use of appropriate neural network control methods to address formation requirements. The paper considers distributed cooperative formation control using a leader-follower approach. The paper also employs neural networks to overcome control challenges while dealing with complex systems or complex conditions. The neural network model was designed and the leader-follower formation control protocol was proposed. The sufficient conditions for the system stability were derived using Lyapunov stability theory, graph theory, and state space methods. By simulating the results of this study, the main data of the formation process can be observed to analyze and verify whether the system meets the requirements. Finally, by using an example of 16 agents to generate a hexagonal formation, it is verified that the system achieves consistency, stability, reliability, and accuracy in the cooperative formation.