Hu Xin, Wei Xinjiang, Gong Qingtao, Gu Jianzhong
School of Mathematics and Statistics Science, Ludong University, Yantai, Shandong, 264025, PR China.
School of Mathematics and Statistics Science, Ludong University, Yantai, Shandong, 264025, PR China.
ISA Trans. 2021 Aug;114:72-81. doi: 10.1016/j.isatra.2020.12.044. Epub 2020 Dec 29.
This work realizes the adaptive neural disturbance rejection for the leader-follower cooperative synchronization of surface ships with model perturbations and ocean disturbances without leader velocity measurements. The virtual ship alleviates the requirements on leader ship's velocities such that the information requirements are only position and heading on the leader ship. The adaptive neural networks approximate model perturbations. The robustifying term attenuates neural network approximation errors. The adaptive neural network-based disturbance observer achieves the disturbance rejection which is integrated with the dynamic surface control technique. The supply ship synchronization control system is ensured to be practical stable. The synchronization control realizes the ship's cooperative synchronization navigation. Simulations with comparisons validate the synchronization scheme.
这项工作实现了水面舰艇在存在模型扰动和海洋干扰且无领导者速度测量情况下的自适应神经干扰抑制，用于领导者 - 跟随者协同同步。虚拟舰艇减轻了对领导者速度的要求，使得对领导者舰艇的信息需求仅为位置和航向。自适应神经网络逼近模型扰动。鲁棒项减弱神经网络逼近误差。基于自适应神经网络的干扰观测器实现了干扰抑制，并与动态面控制技术相结合。确保补给船同步控制系统实际稳定。同步控制实现了舰艇的协同同步导航。对比仿真验证了同步方案。
