Cooperative formation control for multiple surface vessels based on barrier Lyapunov function and self-structuring neural networks

Abstract

This paper studies the cooperative formation control problem of multiple surface vessels based on the barrier Lyapunov function and self-structuring neural networks with uncertainties and unknown disturbances. To constrain and prevent a consistency error that is too large, a tan-type barrier Lyapunov function is employed to dynamically restrain the formation of tracking errors. To handle the model uncertainties, self-structured neural networks are used to approximate the unknown parameters of the dynamics model and to avoid large computational burden. An adaptive law is developed to estimate and compensate for unknown disturbances and neural network approximation errors. Under the proposed distributed cooperative formation control law, formation behavior among vessels can be achieved through any directed communication topological network and an inaccurate model of each vessel. All signals in the closed-loop system are proven to be semi-globally uniformly ultimately bounded on the initial bounded conditions, and the formation tracking error converges to a small neighborhood of origin. The simulations evaluate the performance of the proposed controller, and verify the effectiveness of the proposed method.