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非线性非最小相位船舶舵减摇系统的滑模控制
引用本文:朱云浩, 刘志全, 高迪驹. 基于神经网络的鲁棒自适应舵减摇控制[J]. 中国舰船研究, 2023, 18(3): 86–93, 103. doi: 10.19693/j.issn.1673-3185.02699
作者姓名:朱云浩  刘志全  高迪驹
作者单位:1.上海海事大学 航运技术与控制工程交通运输行业重点实验室,上海 201306
基金项目:国家自然科学基金资助项目(52001197,52171313)
摘    要:  目的  针对存在系统未知非线性函数和外界随机扰动的欠驱动水面船舶舵减摇控制问题,提出一种基于多层循环神经网络的自适应非奇异快速终端滑模舵减摇控制器。  方法  首先,针对传统滑模控制中存在的奇异性和收敛性问题,引入非奇异快速终端滑模面,并在假设船舶模型已知的情况下设计滑模控制律;接着,对传统径向基神经网络进行改进,并利用改进后的神经网络去逼近系统未知非线性函数,以解决船舶航行时模型难以确立的问题,提高控制精度;然后,应用Lyapunov理论证明闭环系统的稳定性和有限时间收敛性,并推导出神经网络参数的自适应律;最后,对一艘多用途海军舰艇进行数值仿真分析。  结果  结果显示,当船舶处于航向保持工况时,所提出的控制器减摇率为50.41%,与非奇异快速终端滑模控制器相比提升了19.2%;当船舶处于变航向工况时,所提出的控制器减摇率为23.46%,与非奇异快速终端滑模控制器相比提升了12.59%。  结论  该方法可以为欠驱动船舶舵减摇控制设计提供参考。

关 键 词:欠驱动船舶  舵减摇  神经网络  非奇异快速终端滑模控制  有限时间收敛性
收稿时间:2021-12-08
修稿时间:2022-03-08

Ship roll damping control
ZHU Y H, LIU Z Q, GAO D J. Robust adaptive rudder roll stabilization control based on neural network[J]. Chinese Journal of Ship Research, 2023, 18(3): 86–93, 103. doi: 10.19693/j.issn.1673-3185.02699
Authors:ZHU Yunhao  LIU Zhiquan  GAO Diju
Affiliation:1.Key Laboratory of Transport Industry of Marine Technology and Control Engineering, Shanghai Maritime University, Shanghai 201306, China
Abstract:  Objectives  In this study, an adaptive non-singular fast terminal sliding mode rudder roll stabilization controller based on a multiple-layer recurrent neural network ( MLRNN) is proposed for the rudder roll stabilization control of an underactuated surface ship with unknown nonlinear system functions and random external disturbances.   Methods  First, in view of the singularity and convergence problems in traditional sliding mode control, a non-singular fast terminal sliding surface is introduced, and the sliding mode control law is designed under the assumption that the ship model is known. The traditional radial basis function neural network (RBFNN) is then improved and used to approximate unknown nonlinear system functions in order to solve the problem of ship models being difficult to establish when the ship is sailing while also improving the control accuracy. The stability and finite time convergence of the system are proven by the Lyapunov theory, and the adaptive laws of the neural network parameters are derived. Finally, a numerical simulation analysis of a multi-purpose naval ship is carried out.   Results  The results show that when the ship is under the course keeping condition, the roll reduction rate of the proposed controller is 50.41%, which is 19.2% larger than that of the non-singular fast terminal sliding mode controller (NFTSMC). When the ship is under the course changing condition, the roll reduction rate of the proposed controller is 23.46%, which is 12.59% larger than that of the NFTSMC.   Conclusions  This method can provide valuable references for the design of underactuated ship rudder roll stabilization controllers.
Keywords:underactuated ship  rudder roll stabilization  neural network  non-singular fast terminal sliding mode controller  finite time convergence
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