Adaptive neural formation control for underactuated unmanned surface vehicles with collision and connectivity constraints

The aim of this paper is to address the leader-follower formation control problem for a group of underactuated unmanned surface vehicles (USVs) with non-diagonal inertia matrix subject to modeling uncertainties and limited sensing capabilities. No communication is required among the USVs, but every USV is only equipped with on-board sensors to measure the line-of-sight (LOS) range and the relative bearing angle. The connectivity preserving constraint arisen from the limited sensing capability and the collision avoidance constraint resulting from the safety requirement are imposed on the LOS range and the relative bearing angle between every follower and its leader. These constraints are subsequently incorporated into the tan-type barrier Lyapunov function-based formation control design. Every USV reconstructs the velocity of its leader using the high-gain observer based solely on the available LOS range and relative bearing angle. Based on coordinate transformation, backstepping procedure, dynamic surface control (DSC) technique, and neural network approximation, a singularity-free formation controller is then developed, which guarantees the boundedness of all the closed-loop system signals and achieves satisfaction of prescribed performance specifications on the formation errors. Simulations are performed to verify the effectiveness of the formation control strategy.