|本期目录/Table of Contents|

[1]万 磊,唐文政,李岳明.智能水下机器人BP神经网络S面控制[J].工业仪表与自动化装置,2019,(02):13-18.[doi:1000-0682(2019)02-0000-00]
　WAN Lei,TANG Wenzheng,LI Yueming.BP neural network S plane control for autonomous underwater vehicle[J].Industrial Instrumentation & Automation,2019,(02):13-18.[doi:1000-0682(2019)02-0000-00]
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智能水下机器人BP神经网络S面控制

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2019年02期
页码:
13-18
栏目:
出版日期:
2019-04-15

文章信息/Info

Title:
BP neural network S plane control for autonomous underwater vehicle
作者:
万 磊; 唐文政; 李岳明
哈尔滨工程大学 水下机器人技术重点实验室，哈尔滨 150001
Author(s):
WAN Lei;  TANG Wenzheng;  LI Yueming
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)
关键词:
智能水下机器人; S面控制; BP神经网络; 运动控制
Keywords:
autonomous underwater vehicle;  S plane control;  BP neural network;  motion control
分类号:
TP24
DOI:
1000-0682(2019)02-0000-00
文献标志码:
A
摘要:
针对智能水下机器人传统S面控制器参数设置过程依赖经验且设置不当将严重影响运动控制效果的问题，设计了BP神经网络S面控制器，由神经网络正向传播输出S面控制器参数，并在反向传播中实现参数的在线整定。采用某微小型智能水下机器人模型仿真实验的结果表明，该控制器能够自主完成控制参数初始化与调整，具有收敛速度快、超调与稳态误差小、干扰条件下能够迅速恢复稳定等优点，可以为实际工程中运动控制器设计提供参考。
Abstract:
As a practical motion controller for autonomous underwater vehicle, S plane controller needs to set its control parameters manually relying on experience, of which improper setting will lead to adverse effect on precision and effect. A controller combining BP Neural Network with S Plane Control is proposed. In BP neural network S plane controller, the parameters of S plane controller are outputted from the forward propagation of BP neural network, and on-line tuning of parameters is realized in backpropagation. The simulation experiment using a micro intelligent underwater vehicle model shows that the controller can complete the process of initializing and adjusting parameters independently. In the control of velocity and heading, it has the advantages of fast convergence rate, small overshoot and steady-state error, and the ability to recover stability quickly under disturbance. The controller can achieve good motion control effect and improve the intelligent level of motion control of autonomous underwater vehicle, which provides a reference for the design of motion controller in practical engineering.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2018-09-25
基金项目：国家自然科学基金项目(51509057)；中央高校基本科研业务费专项资金资助项目(HEUCF180102)
作者简介：万磊(1946)，男，黑龙江省哈尔滨市人，博士，研究员，研究方向为水下机器人运动控制理论与方法，水下目标检测与识别技术，人工智能理论在水下机器人领域的应用，水下传感器数据融合技术和多水下机器人协同技术。
更新日期/Last Update: 2019-04-15