Improved decentralized finite-time formation control of underactuated USVs via a novel disturbance observer

Abstract

This note investigates the decentralized finite-time formation control of underactuated unmanned surface vessels (USVs) in the presence of model uncertainty and environmental disturbance. In the algorithm, a novel adaptive finite-time disturbance observer (AFTDO) is incorporated into the proposed control strategy that enhances its robustness to the environmental disturbance. By fusion of neural network (NN) and minimal learning parameterization (MLP) techniques, the AFTDO is constructed without priori information about the ship model and the upper bound of disturbance. On the basis of undirected graph, the decentralized control law is developed with local information from neighboring USVs. Specially, since the AFTDO and the decentralized control law share the same set of NN, the whole algorithm is with merits of concise form and less adaptive parameters. Such design contributes to smaller computation load and facilitates the implementation of the algorithm in ocean engineering. The semi global finite-time uniformly boundedness (SGFTUB) of the closed-loop system is proved by the Lyapunov theory. Numerical simulations and comprehensive comparisons are conducted to demonstrate the remarkable performance and superiority of the proposed algorithm.