Neuro-Adaptive Formation Control and Target Tracking for Nonlinear Multi-Agent Systems With Time-Delay

This letter proposes an adaptive neural network-based backstepping controller that uses rigid graph theory to address the distance-based formation control problem and target tracking for nonlinear multi-agent systems with bounded time-delay disturbance. The radial basis function network (RBFNN) is used overcome compensate unknown nonlinearity disturbance in system dynamics. effect of state agents alleviated by using appropriate signal designed based on specific Lyapunov Young's inequality. (NN) weights tuning law derived this function. An upper bound singular value normalized rigidity matrix introduced, uniform ultimate boundedness (UUB) distance error rigorously proven stability theory. Finally, performance effectiveness proposed method are validated through simulation results systems. Comparisons between existing, displacement-based provided evaluate suggested method.