Neural network observer-based leader-following consensus of heterogenous nonlinear uncertain systems

This paper considers the leader-following consensus of heterogeneous multiple agents with high-order nonlinear uncertain systems. Previous results in this field consider the leader’s dynamics as disturbances, which may lead to oscillation, overshoot, or even unstability of the whole system due to high-gain consensus control. This study considers neural network (NN) observer-based leader-following consensus which can avoid high gain at the consensus control. First of all, distributed NN-based leader observers are designed to estimate the leader’s states and nonlinear dynamics. Theoretical analysis by Lyapunov theory is followed to illustrate the effectiveness of the observers. Then, to obtain the leader-following consensus, NN controllers are designed for the following agents to track the corresponding leader observers. Theoretical proof and simulation results illustrate that the leader-following consensus errors are uniformly ultimately bounded (UUB) and can be made arbitrarily small by an appropriate choice of corresponding gains.