Distributed finite-time neural network observer-based consensus tracking control of heterogeneous underwater vehicles

Abstract

This note scrutinizes the distributed finite-time tracking problem of heterogeneous multi-agent systems with uncertain dynamics and environmental disturbances. To estimate the exogenous disturbances, an adaptive neural network-based disturbance observer is developed in which the approximation error of the neural network is compensated using adaptation laws. Based on finite-time stability theory, a distributed finite-time observer is proposed for each follower in order to estimate the leader's position. Moreover, an adaptive dynamic sliding mode-based controller is also developed for each follower, which ensures finite-time convergence of the tracking error between the leader and the follower. Incorporating adaptive control into the developed composite control structure leads to reducing the chattering, as well as enhancing the robustness and accuracy. Stability analysis and finite-time convergence of the tracking error are performed using the Lyapunov theory and multiple time scale principle, respectively. Finally, simulations and comparisons are performed in order to verify the effectiveness of the developed control algorithm for a group of autonomous underwater vehicles (AUVs).