IEEE Trans Cybern. 2017 Jul;47(7):1641-1651. doi: 10.1109/TCYB.2016.2554621. Epub 2016 May 11.
In this paper, we consider the trajectory tracking of a marine surface vessel in the presence of output constraints and uncertainties. An asymmetric barrier Lyapunov function is employed to cope with the output constraints. To handle the system uncertainties, we apply adaptive neural networks to approximate the unknown model parameters of a vessel. Both full state feedback control and output feedback control are proposed in this paper. The state feedback control law is designed by using the Moore-Penrose pseudoinverse in case that all states are known, and the output feedback control is designed using a high-gain observer. Under the proposed method the controller is able to achieve the constrained output. Meanwhile, the signals of the closed loop system are semiglobally uniformly bounded. Finally, numerical simulations are carried out to verify the feasibility of the proposed controller.
本文针对存在输出约束和不确定性的海面船舶轨迹跟踪问题展开研究。采用非对称障碍李雅普诺夫函数来处理输出约束。为了处理系统不确定性，应用自适应神经网络来逼近船舶的未知模型参数。本文提出了全状态反馈控制和输出反馈控制。在所有状态都已知的情况下，通过使用 Moore-Penrose 伪逆设计状态反馈控制律，而输出反馈控制则采用高增益观测器设计。在所提出的方法下，控制器能够实现约束输出。同时，闭环系统的信号是半全局一致有界的。最后，通过数值仿真验证了所提出控制器的可行性。
