输入饱和约束下自适应RBF神经网络非线性反馈船舶航向控制
Nonlinear feedback control with adaptive RBF neural network for ship course under input saturation constraints
苏文学 1孟祥飞 2张强1
作者信息
- 1. 山东交通学院航运学院,山东 威海 264200
- 2. 上海海事大学商船学院,上海 201306
- 折叠
摘要
针对输入饱和约束下外界扰动和模型不确定情况下的船舶航向跟踪控制问题,提出一种自适应径向基函数(radial basis function,RBF)神经网络非线性反馈航向跟踪控制方法.利用自适应RBF神经网络对外界扰动和模型不确定项进行估计,并利用最小学习参数法减少计算量;将一个具有误差增益反相关特征的非线性函数嵌入控制律中,设计一种非线性反馈控制方法;利用李雅普诺夫理论证明所有信号在考虑外界扰动和模型不确定的船舶航向跟踪控制系统中都是一致有界的.通过仿真和比较,验证了所设计控制方法的有效性.所做研究可为输入饱和约束下船舶航向跟踪控制提供参考,具有工程实际意义.
Abstract
Aiming at the issue of ship course tracking control with external disturbances and model uncertainties under input saturation constraints,the nonlinear feedback control method with the adaptive radial basis function(RBF)neural network for ship course tracking is proposed.The adaptive RBF neural network is used to estimate external disturbances and model uncertainties,and the minimum learning parameter method is used to reduce the computation complexity;the nonlinear function with the error gain inverse correlation characteristic is embedded in the control law to design a nonlinear feedback control method;based on Lyapunov theory,it is proved that all signals are uniformly bounded in the ship course tracking control system considering external disturbances and model uncertainties.The effectiveness of the designed control method is verified through simulation and comparison.The research can provide reference for ship course tracking control under input saturation constraints and is of practical engineering significance.
关键词
船舶航向跟踪/径向基函数(RBF)神经网络/非线性反馈控制/输入饱和Key words
ship course tracking/radial basis function(RBF)neural network/nonlinear feedback control/input saturation引用本文复制引用
基金项目
国家自然科学基金(51911540478)
山东省重点研究发展计划(2019JZZY020712)
出版年
2024
NETL
NSTL
万方数据