Formation Control and Target Tracking for a Class of Nonlinear Multi-Agent Systems Using Neural Networks

This paper proposes a neural network-based back-stepping controller to address the distance-based formation control problem and target tracking for a class of nonlinear multi-agent systems in Brunovsky form using rigid graph theory. The radial basis function neural network (RBFNN) is used to ensure the system stability in the presence of unknown nonlinearity and disturbance in the system dynamics. A Lyapunov function is used to derive the neural network (NN) weights tuning law. The uniform ultimate boundedness (UUB) of the formation distance errors is rigorously proven based on the Lyapunov stability theory. Finally, the effectiveness of the proposed method is shown using the simulation results on a class of nonlinear multi-agent systems. A comparison between the proposed distance-based method and the existing displacement-based method is conducted to evaluate the performance of the proposed method.