Adaptive neural formation control of autonomous underactuated surface vessels based on disturbance observer with leader–follower strategy

Abstract

In this paper, a leader–follower formation control strategy is presented based on adaptive neural network and disturbance observer, which is aimed at resolving model uncertainties as well as the time-varying disturbances for autonomous underactuated surface vessels. The model uncertainties which can be expressed by unknown nonlinear functions are approximated and compensated by the adaptive neural network. The disturbance observer introduced can estimate time-varying disturbances and compensate them to the feedforward control loop, so as to make the external time-varying disturbances suppressed and the robustness of controller against the disturbances improved. The dynamic surface control technology is applied in the procedure of designing the controller through utilizing the backstepping method, which solves the computational explosion of the derivative of virtual control signals. Finally, through Lyapunov analysis, the stability of adaptive neural formation control system is proved and all the error signals uniformly converge to a very small range ultimately. The excellent performance of the presented formation control strategy is demonstrated through numerical simulations.