摘要
针对全驱动海洋水面船舶的区域到达控制问题,结合反演法,提出了一种基于RBF神经网络的自适应区域达到控制算法;与传统的定点控制方法不同,区域到达控制概念中将控制目标设定为以期望点为中心的空间区域;控制器的设计实现主要采用了目标势能函数、反演设计方法和李雅普诺夫稳定性理论;利用RBF神经网络,对全驱动船舶模型中的不确定函数及外部环境扰动进行有效逼近;通过李雅普诺夫理论,对所提出的船舶区域到达控制算法进行了稳定性分析,并证明了闭环系统的一致最终有界性;仿真研究结果验证了所设计的区域到达控制器的有效性。
In this paper,adaptive region reaching control is developed for fully actuated ocean surface ships based on the RBF neural network and the backstepping techniques.Different from the traditional setpoint control,the objective in region reaching control is specified as a desired spatial region.The controller design is achieved with the help of target potential function,backstepping design methodology and Lyapunov stability theory.The uncertainties and unknown perturbations in the surface ship is estimated by using of the RBF neural network.The stability analysis of the proposed region reaching control method is achieved by the Lyapunov theory,and the global uniform ultimate boundedness of the closed-loop system can be guaranteed.The simulation results verify the effectiveness of the proposed approach.
作者
孙晓明
徐庆
郑兴伟
霍海波
田中旭
Sun Xiaoming;Xu Qing;Zheng Xingwei;Huo Haibo;Tian Zhongxu(School of Engineering Science and Technology,Shanghai Ocean University,Shanghai 201306,China;School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China)
出处
《计算机测量与控制》
2020年第4期105-109,共5页
Computer Measurement &Control
基金
国家自然科学基金(51775329)。
关键词
全驱动船舶
区域到达
反演法
神经网络
fully actuated ocean surface ships
region reaching
backstepping
neural network
