中国科学院沈阳自动化研究所机构知识库
Advanced  
SIA OpenIR  > 水下机器人研究室  > 学位论文
题名: 复杂海洋环境下水下机器人控制问题研究
其他题名: Study on Control Problems for Underwater Vehicles in Complicated Oceanic Environment
作者: 邢志伟
导师: 封锡盛
分类号: TP242.3
关键词: 水下机器人 ; 神经网络 ; 自适应控制 ; 动力定位
索取号: TP242.3/X63/2003
学位专业: 机械电子工程
学位类别: 博士
答辩日期: 2003-06-26
授予单位: 中国科学院沈阳自动化研究所
学位授予地点: 中国科学院沈阳自动化研究所
作者部门: 水下机器人技术研究室
中文摘要: 复杂海洋环境下实现水下机器人的稳定高精度控制是目前水下机器人研究所面临的最大的挑战之一。水下机器人作业过程中载体本身动力学模型的时变性、非结构不确定性和非线性、海洋环境干扰的不确定性和时变性、水声定位系统的反馈延迟以及部分状态量的不可测量问题给水下机器人的控制问题带来极大的挑战。针对以上问题,本文从以下几个方面开展研究: 1. 根据自适应逆控制思想和预测-校正控制策略,利用神经网络的学习和非线性逼近能力,提出基于神经网络的水下机器人自适应逆控制策略。同时在自适应算法中引入微分项,理论分析和仿真研究表明,微分项的引入提高了系统自适应过程的稳定性。 2. 为保证上述算法的初始稳定性和神经网络的泛化学习能力,需要预先的离线学习过程,同时保证参考输入信号满足持续激励条件,这需要大量的实验取得样本数据。为避免这一问题,根据目前在基于神经网络稳定控制方面的研究成果,提出基于状态反馈的水下机器人神经网络直接自适应控制理论,该算法无需离线过程和持续激励条件。基于Lyapunov稳定性理论的设计方法可以保证系统的稳定性和控制器参数的有界性。 3. 针对水下机器人系统部分状态不可测问题,提出基于观测器的水下机器人神经网络自适应控制策略。该控制策略利用速度观测器理论,根据系统测得的位置信息进行系统相应控制回路的速度估计。在分析基于观测器的机械手自适应控制策略的基础上,根据水下机器人动力学模型的特殊性,利用基于Lyapunov的稳定性定理,从理论上给出了该控制算法的稳定条件和稳定域。仿真及水池实验验证了算法的有效性。 4. 利用上述基于状态反馈和基于观测器的水下机器人神经网络稳定自适应控制算法,对基于超短基线/多普勒的水下机器人动力定位问题进行研究。对于水声定位系统产生的信号延迟问题,提出采用卡尔曼滤波算法估计水下机器人的位置信息,基于新息的Q,R自适应算法保证了卡尔曼滤波估计的最优性,并给出了基于估计的水下机器人动力定位系统控制结构,仿真结果验证了算法和控制结构的有效性。
英文摘要: Precise and stable control of underwater vehicle in complicated oceanic environment becomes one of the most challenging problems. The facts that make it difficult to control the underwater vehicle precisely and stably are as the followings: nonlinearity, time-vary and non-structure uncertainty of the dynamics of underwater vehicles; the time-vary and uncertainty disturbance come from oceanic environment; the feedback time-delay due to the hydrodynamic positioning system and some states are of hard to measure during control. Therefore, it is difficult to achieve high performance by utilizing a conventional control algorithm. The research in this dissertation is based on the problems mentioned above. The main research problems are as the followings:1. An adaptive inverse control based underwater vehicles neural network control algorithm is proposed. The neural network is used to deal with any changes come from underwater vehicles and environment disturbance taking advantage of its online learning ability. A derivative term is introduced to the adaptive algorithm of controller. The result of numerical simulation and theoretical analysis has shown that the robust of the adaptive algorithm is greatly improved due to the introduction of the derivative term.2. Two problems have to be considered to guarantee satisfactory performance of the control scheme mentioned above in terms of small tracking error and neural network weight bounded. First uncertainty on how to initialize the NN weights that can guarantee the stability of the close-loop system at initial period leads to the necessity for preliminary off-line tuning. Second, the system states are required to be uniformly persistently exciting to guarantee the bound of NN weights. These conditions are all difficult to be checked/guaranteed in a practical application. An underwater vehicle neural network stable control algorithm based on state feedback is proposed in this thesis. Both the convergence of tracking error and bound of NN weights are guaranteed by using Lyapunov approach. No prior off-line training and persistent are required.3. Not all of the states of underwater vehicle can be measured considering the cost or feasibility in practical application. An observer-based neural network control scheme is proposed in this thesis to solve the velocity feedback problem. In this control scheme, the observer utilizes the position information for reconstructing the velocity signal and a multi-layer neural network is used to approximate the nonlinearity of the underwater system. The stable conditions and attraction region of the proposed control scheme is provided by using Lyapunov approach according to the dynamics of underwater vehicle. The effectiveness of this control scheme is demonstrated by numerical simulation and pool experiment.4. The problem of USBL/Doppler based underwater vehicle dynamic positioning is studied by utilizing the state feedback and observer based neural network control scheme mentioned above. An underwater vehicle position estimation algorithm based on Kalman filter is proposed to deal with the problem of USBL position measure feedback time-delay. The optimization of position estimation is guaranteed by utilizing the innovation-based adaptive Kalman filter estimation algorithm. The control structure base on estimation is provided and the performance is verified by numerical simulation.
语种: 中文
产权排序: 1
内容类型: 学位论文
URI标识: http://ir.sia.cn/handle/173321/9482
Appears in Collections:水下机器人研究室_学位论文

Files in This Item: Download All
File Name/ File Size Content Type Version Access License
复杂海洋环境下水下机器人控制问题研究.pdf(3788KB)----限制开放View Download

Recommended Citation:
邢志伟.复杂海洋环境下水下机器人控制问题研究.[博士学位论文].中国科学院沈阳自动化研究所.2003
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[邢志伟]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[邢志伟]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit
文件名: 复杂海洋环境下水下机器人控制问题研究.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 
评注功能仅针对注册用户开放,请您登录
您对该条目有什么异议,请填写以下表单,管理员会尽快联系您。
内 容:
Email:  *
单位:
验证码:   刷新
您在IR的使用过程中有什么好的想法或者建议可以反馈给我们。
标 题:
 *
内 容:
Email:  *
验证码:   刷新

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2016  中国科学院沈阳自动化研究所 - Feedback
Powered by CSpace