Distributed Prescribed-Time Formation Control for Underactuated Surface Vehicles With Input Saturation: Theory and Experiment
Yueying Wang, Xiang Liu, Zhengtian Wu, Chuangyin Dang
Abstract
In this paper, we investigate a neural adaptive formation control problem for underactuated unmanned surface vehicles (USVs). Considering the limitation of communication distance and the security of formation systems, collision-free and connectivity maintenance are guaranteed by defining a prescribed-time tuning function and proper error transformation. Furthermore, a new nonlinear first-order filter, solving the complexity problem, is designed to promote the system performance. Subsequently, neural networks (NNs) are used to approximate USVs’ dynamics and their transient performance is improved by prediction error. By blending prediction errors and neural approximation, it is guaranteed the general external disturbances and approximation errors are compensated via constructed disturbance observers (DOs), simultaneously. Meanwhile, utilizing the minimal number of learning parameters (MNLPs) methodology, the number of NNs’ learning parameters can be significantly reduced. It is rigorously proved that all signals in the closed-loop system are bounded via Lyapunov stability theorem. Finally, simulation and experimental studies are presented to verify the effectiveness and advantages of theoretical results.