Distributed Cooperative Formation Control of Nonlinear Multi-Agent System (UGV) Using Neural Network

Abstract

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 (uncertainties dynamic model). It considers an adaptive leader-follower distributed cooperative formation control based on neural networks (NNs) developed for a class of second-order nonlinear multi-agent systems and neural networks Neural networks are used to compute system data that inputs layer (position, velocity), hidden layers, and output layer. Through collaboration between leader-follower approaches and neural networks with complex systems or complex conditions receive an effective cooperative formation control method. The sufficient conditions for the system stability were derived using Lyapunov stability theory, graph theory, and state space methods. By simulation, the results of this study can be obtained from the main data of the multi-agent system in formation control and verified that the system can process consistency, stability, reliability, and accuracy in cooperative formation.