Synchronized Tracking Control of Multi-Agent System with Limited Information

In this paper, synchronized tracking control is considered for multiple agents with unknown system dynamics, while the desired trajectory is only available to portion of the team members. Using the weighted average of the neighbors' outputs, adaptive neural network (NN) tracking control is designed for each agent. Rigid mathematical proof was provided for the proposed algorithm based on the Lyapunov analysis. It is shown that, under the proposed NN control, the output tracking error of each agent converges to an adjustable neighborhood of the origin. Simulations of synchronized altitude tracking of multiple unmanned helicopters are provided to demonstrate the effectiveness of the approaches presented.