In this article, an adaptive radial basis function neural network scheme for trajectory tracking control of surface vehicles is proposed. Under complex uncertainties, the proposed controller is designed by combining radial basis function neural network and finite-time control algorithm. Using the novel controller, the stability of accurate trajectory tracking can be ensured and the robustness of control system can be improved. Theoretical proof is proposed by Lyapunov function that the radial basis function neural network controller can make surface vehicle to accurately track desire trajectory steadily. Simulation studies conducted on a prototype CyberShip II demonstrate remarkable performance of proposed control scheme.

本文提出了一种基于自适应径向基函数神经网络的地面车辆轨迹跟踪控制方案。在复杂的不确定性条件下，该控制器是结合径向基函数神经网络和有限时间控制算法设计的。使用新颖的控制器，可以确保精确的轨迹跟踪的稳定性，并可以提高控制系统的鲁棒性。利用李雅普诺夫函数提出的理论证明，径向基函数神经网络控制器可以使地面车辆稳定，稳定地跟踪期望轨迹。在原型Cyber​​Ship II上进行的仿真研究证明了拟议控制方案的卓越性能。