Convergence Analysis on Multi-AUV Systems With Leader-Follower Architecture

In multi-autonomous underwater vehicle (multi-AUV) systems, the convergence rate is characterized by the pace of consistency of the key state information for each member. The topology with leader–follower architecture is designed as a combination of an undirected graph between followers and a digraph between leaders and followers. An overview of influences on convergence rate of the second-order consensus algorithm is elaborated in three aspects, along with the main contributions in this paper. Specifically, the explicit expression of the maximum convergence rate is established based on the root locus method, and then, the effects of control parameters on the convergence rate are analyzed. Moreover, the influences of network topologies on the convergence rate are investigated from the view of adjusting the existing connectivity, changing the weights on links, and utilizing hierarchical structure. The combination of consensus and filtering algorithm is also an approach to enhance the capacity of multi-AUV systems. In order to eliminate the accumulated errors in the process of dead reckoning, a collaborative navigation model is presented, and then, a localization approach based on consensus-unscented particle filter algorithm is proposed. Simulations results are provided to verify location performance under the assumption of Gaussian white noise in the systems. In addition, the influences of the topologies on positioning accuracy are explored.