Adaptive Flocking Control of Uncertain Networked Agents for Tracking a Time-Varying Virtual Leader

This paper studies the adaptive flocking tracking problem of multi-agent systems. An adaptive flocking control scheme based on a radial basis neural network (RBFNN) is designed for multi-agent systems with nonlinear model uncertainties and a time-varying virtual leader. The model uncertainties are multifarious and the several states of the virtual leader are unknown. By introducing RBFNN with online updating of a weight matrix, the model uncertainties problem which may cause system destabilization is solved, and a distributed time-varying leader high-order information estimator is designed. Furthermore, the control protocol composed of the flocking control term, uncertainties compensation term, and state estimation term is proposed to accomplish adaptive flocking tracking control. The stability of the closed-loop system is proven based on the Lyapunov theory. Finally, the effectiveness of the proposed adaptive control scheme is verified by some simulation results.