Adaptive finite-time consensus protocols for multi-agent systems by using neural networks

This study investigates the adaptive finite-time consensus protocols for a class of second-order multi-agent systems with unknown non-linear dynamics. Mainly due to the presence of more general unknown non-linearities, the finite-time consensus of multi-agent systems is rather difficult to reach. Radial basis function neural networks are used to approximate the unknown functions. Under some appropriate assumptions, distributed consensus protocols and adaptive laws are designed for all agents. It is proved that the position errors and the velocity errors between any two agents converge to a small neighbourhood of the origin in finite time. Finally, an example of finite-time consensus control for multiple robot systems is given to demonstrate the effectiveness of the proposed method.