基于改进PID神经网络算法的AUV垂直面控制

doi:10.16182/j.issn1004731x.joss.17-9168

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (2): 229-235.doi: 10.16182/j.issn1004731x.joss.17-9168

• 仿真支撑平台/系统技术 • 上一篇下一篇

基于改进PID神经网络算法的AUV垂直面控制

黄茹楠, 丁宁

燕山大学电气工程学院,河北秦皇岛 066004

收稿日期:2017-12-12 修回日期:2018-11-08 出版日期:2020-02-18 发布日期:2020-02-19
作者简介:黄茹楠(1966-),女,河北,博士,副教授,研究方向为计算机仿真、水下航行器运动控制;丁宁(1993-),男,黑龙江,硕士,研究方向为水下航行器运动控制。
基金资助:
国家自然科学基金(61472341)

AUV Vertical Plane Control Based on Improved PID Neural Network Algorithm

Huang Runan, Ding Ning

College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

Received:2017-12-12 Revised:2018-11-08 Online:2020-02-18 Published:2020-02-19

摘要/Abstract

摘要： 针对一类小型低速自主水下航行器(AUV)的垂直面运动控制问题,设计了一种改进的PID神经网络控制器,实现对水下航行器在垂直面内深度和俯仰角的全局控制。利用REMUS水下航行器模型搭建了Simulink下AUV垂直面仿真控制系统,仿真结果表明,改进的控制方法克服了原方法中饱和区过大的问题,具有良好的动态性能同时能够适应不同的学习速率和网络初始权重,对水下航行器的工程实际应用具有一定参考价值。

关键词: 低速水下航行器, 改进PID神经网络, 非线性系统, 多变量全局控制, Simulink仿真

Abstract: An improved PID neural network controller is designed for the movement control of a small low-speed autonomous underwater vehicle (AUV) in vertical plane, and the global control of the depth and pitch angle of the underwater vehicle in vertical plane is obtained. The AUV simulation control system is built by using REMUS underwater vehicle model in Simulink. The simulation results show that the improved control method with better dynamic performance has solved the original excessive saturation issue, and can adapt to different learning rates and network initial weight,and is of certain reference value to the practical application of the underwater vehicle.

Key words: low-speed underwater vehicle, improved PID neural network, nonlinear system, multivariable global control, simulink simulation

中图分类号:

TP273.2

黄茹楠, 丁宁. 基于改进PID神经网络算法的AUV垂直面控制[J]. 系统仿真学报, 2020, 32(2): 229-235.

Huang Runan, Ding Ning. AUV Vertical Plane Control Based on Improved PID Neural Network Algorithm[J]. Journal of System Simulation, 2020, 32(2): 229-235.

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基于改进PID神经网络算法的AUV垂直面控制

AUV Vertical Plane Control Based on Improved PID Neural Network Algorithm

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