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作 者:徐海祥[1,2] 胡聪[1,2] 余文曌 姚国全 XU Haixiang;HU Cong;YU Wenzhao;YAO Guoquan(Key Laboratory of High Performance Ship Technology,Ministry of Education,Wuhan University of Technology,Wuhan 430063,China;School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China)
机构地区:[1]武汉理工大学高性能舰船技术教育部重点实验室,湖北武汉430063 [2]武汉理工大学船海与能源动力工程学院,湖北武汉430063
出 处:《系统工程与电子技术》2022年第11期3496-3504,共9页Systems Engineering and Electronics
基 金:国家自然科学基金(51879210,51979210);中央高校基本科研业务费专项资金(2019III040,2019III132CG)资助课题。
摘 要:针对浮力调节机构约束下无人水下航行器(unmanned underwater vehicle, UUV)的变深控制问题,提出一种基于正交神经网络饱和补偿器的自适应动态面控制方法。首先,建立考虑执行机构动态特性的UUV数学模型。在此基础上,采用反步法和非线性跟踪微分器设计动态面控制器,同时引入线性扩张状态观测器(linear extended state observer, LESO)在线估计浮力变化与模型不确定性引起的干扰,继而在控制器中进行补偿。然后,基于正交神经网络设计饱和补偿器,并证明闭环系统所有误差一致最终有界。仿真结果表明,与现有的动态面控制方法相比,所提方法在浮力调节机构约束下,具有较好的动态性能与稳态精度。Aiming at the problem of variable depth control of unmanned underwater vehicle(UUV) constrained by the buoyancy regulator mechanism, an adaptive dynamic surface control method based on the orthogonal neural network saturation compensator is proposed. Firstly, the UUV mathematical model considering the dynamic characteristics of the actuator is established. On this basis, the dynamic surface controller is designed by using the backstepping method and the nonlinear tracking differentiator. The linear extended state observer(LESO) is designed to estimate the disturbance caused by buoyancy change and model uncertainty online. Then compensation is carried out in the controller. Secondly, the saturation compensator is designed based on the orthogonal neural network. It is proved that all the errors of the closed-loop system are consistent and finally bounded. The simulation results show that the proposed method has better dynamic performance and steady-state precision under the constraint of the buoyancy regulator mechanism compared with the existing dynamic surface control method.
关 键 词:浮力调节机构 无人水下航行器 动态面控制器 线性扩张状态观测器 正交神经网络饱和补偿器
分 类 号:TP273[自动化与计算机技术—检测技术与自动化装置]
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