模型不确定下船舶动力定位鲁棒控制器设计  

Robust Controller Design for Ship Dynamic Positioning Under Model Uncertainty

作　　者：朱梦飞 徐海祥[1,2] 余文曌 卢林枫 ZHU Mengfei;XU Haixiang;YU Wenzhao;LU Linfeng(Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430063, China;School of Transportation, Wuhan University of Technology, Wuhan 430063, China)

机构地区：[1]武汉理工大学高性能船舶技术教育部重点实验室,武汉430063 [2]武汉理工大学交通学院,武汉430063

出　　处：《武汉理工大学学报（交通科学与工程版）》2020年第3期522-526,共5页Journal of Wuhan University of Technology（Transportation Science & Engineering）

基　　金：国家自然科学基金项目(51879210);中央高校基本科研业务费专项资金项目(2019Ⅲ040,2019III132CG)资助。

摘　　要：针对具有模型参数不确定性和未知时变环境扰动的船舶动力定位控制问题,为简化设计过程并且使控制输出尽可能平稳,假设扰动上界已知,设计了一种鲁棒控制律.该方法与动态面控制技术结合,减小计算复杂度,易于工程实现;应用正交神经网络估计时变环境扰动;设计鲁棒项补偿模型参数不确定项与神经网络逼近误差,提高控制系统的鲁棒性.应用Lyapunov函数证明了该控制律能使船舶位置收敛到期望位置,同时保证闭环控制系统中所有信号一致最终有界.基于一艘平台供应船模型的仿真结果验证了所提出控制律的有效性.In order to simplify the design process and make the control output as stable as possible,a robust control law was designed on the assumption that the upper bound of disturbance was known.The proposed method was combined with dynamic surface control technology,which reduces the computational complexity and is easy for engineering implementation.The orthogonal neural network was used to estimate the time-varying environmental disturbance,and the robust term was designed to compensate the uncertainty of the model parameters and the approximation error of the neural network,thus improving the robustness of the control system.Lyapunov function was used to prove that the control law can make the ship position converge to the desired position and ensure that all signals in the closed-loop control system were uniformly and ultimately bounded.Simulation results based on a platform supply ship model verify the effectiveness of the proposed control law.

关 键 词：动力定位 模型参数不确定性 鲁棒控制 动态面控制 

分 类 号：U674[交通运输工程—船舶及航道工程]