Observer-based adaptive affine formation maneuver control for underactuated multi-ASV systems

This paper investigates the affine formation maneuver control problem for underactuated multi-autonomous surface vehicle systems subject to limited sensing of neighbor state information and unknown disturbances. First, to address the situation where neighbor velocities are unavailable, a distributed super-twisting sliding mode observer is designed and used to estimate the unavailable velocities. Second, an observer-based affine formation controller is developed, which enables the formation to execute affine maneuvers, such as scaling, rotation, and translation, to navigate through complex environments. Moreover, a radial basis function neural network with an adaptive weight update law is employed to compensate for unknown disturbances. Then, the stability of the observer and the closed-loop system is analyzed, guaranteeing that all tracking errors and the estimation errors of the neural network are uniformly ultimately bounded. Finally, numerical simulations are conducted in a scenario with surface obstacles and disturbances to verify the effectiveness of the proposed observer and controller. The simulation results indicate that the proposed strategy achieves a mean absolute error of 0.310, a root mean square error of 1.201, and an integral absolute error of 21.668 for formation tracking accuracy.