SIA OpenIR  > 水下机器人研究室
面向水下目标抓取的自主遥控水下机器人位姿稳定控制研究
Alternative TitleResearch on Position and Attitude Stability Control of Autonomous and Remotely-operated Vehicle for Underwater Target Grasping
丁宁宁
Department水下机器人研究室
Thesis Advisor唐元贵
Keyword自主遥控水下机器人 协调控制 广义超螺旋算法 离散化
Pages95页
Degree Discipline机械电子工程
Degree Name硕士
2021-05-21
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract水下目标抓取,是一种典型和重要的水下机器人作业内容与应用场景。自主遥控水下机器人(Autonomous and Remotely-operated Vehicle, ARV)是一种融合了自主水下机器人(Autonomous Underwater Vehicle,AUV)和遥控水下机器人(Remotely Operated Vehicle, ROV) 部分技术功能和特点于一体的新型水下机器人,具有自主、遥控和混合等多种操作模式。ARV可携带机械臂,采用坐底或悬浮方式完成一些轻型的水下目标抓取任务。然而当ARV进行悬浮抓取作业时,载体和机械臂在运动学和动力学层面均存在着较严重的耦合作用,对水下目标抓取带来一定的技术难度。此外ARV为了同时兼顾机动性和稳定性,其稳心高配置的相对较小,因而机械臂运动会导致载体位姿发生较大变化;载体的位姿变化反过来又会影响机械臂末端的作业精度,对水下目标的顺利抓取带来了挑战。因此ARV在悬浮抓取作业过程中的的位姿稳定控制对于顺利完成水下目标抓取作业至关重要。本文以作业型全海深自主遥控潜水器“海斗一号”为研究对象,针对其在悬浮状态下开展水下目标抓取时的位姿稳定控制进行研究。本文主要研究内容和结果如下:(1) 首先对载体和机械臂分别进行了运动学和动力学建模,分析了机械臂对载体的扰动,建立了“海斗一号”ARV的系统模型,然后在Simscape环境中搭建了可视化仿真环境。(2) 针对悬浮作业时机械臂抓取水下目标对载体位姿稳定控制的要求,提出了一种复合控制算法,该算法由自适应跟踪微分器、线性扩张状态观测器和改进的自适应广义超螺旋算法组成。自适应跟踪微分器可安排平缓的过渡过程,进而降低系统超调,还可很好地适应不同幅值的阶跃信号;线性扩张状态观测器用来在线估计并补偿扰动,消除了控制器对系统模型的依赖,提升了控制器的鲁棒性;改进的自适应广义超螺旋算法采用新型变增益策略,消除了传统广义超螺旋算法对扰动微分上界的要求。提出的新型变增益策略允许增益按照一定规则减小,有效避免了对增益的过高估计,也避免了增益在设置的最小值附近振荡。最后利用Lyapunov稳定性原理证明了闭环系统的稳定性。(3) 为了实现上述复合控制算法在“海斗一号”上的工程应用,对其离散化实现进行研究。考虑到控制器中广义超螺旋算法的显式离散化会引发额外的离散化抖振问题,本文除了使用常见的显式离散化方法对其进行离散化,还探讨了广义超螺旋算法的半隐式离散化和隐式离散化。(4) 基于海斗一号动力学模型和响应特性,在Simscape环境中进行了对比仿真实验,控制器参数通过多目标优化和手动调节共同获得。仿真结果显示所提出的控制算法很好地控制了载体在机械臂悬浮抓取过程中的位姿稳定。所提出的控制算法相对于原有的自适应广义超螺旋算法具有更好的抖振抑制能力,控制器的增益没有被过高估计,也没有在设定最小值附近振荡。此外所提出的控制算法在进行隐式离散化和半隐式离散化后均提供了更好的抖振抑制能力,且控制器对过高的控制增益不再敏感,参数整定变得容易。
Other AbstractUnderwater target grasping is a typical and important operating content and application scenario of the underwater vehicle. The Autonomous and Remotely-operated Vehicle (ARV) is a new type of underwater vehicle that integrates some technical functions and features of Autonomous Underwater vehicle (AUV) and Remotely Operated Vehicle (ROV). It has autonomous, remotely operated, and hybrid operation modes. The ARV can carry a manipulator, which can perform some lightweight underwater target grabbing tasks in bottom-sitting or hovering mode. However, when the ARV performs the floating grasping operation, there is a serious coupling effect between the vehicle and the manipulator in kinematics and dynamics, which brings a certain technical difficulty for underwater target grasping. In addition, to consider both maneuverability and stability, the metacentric height configuration of ARV is relatively small, so the manipulator movement has a substantial impact on the vehicle's position and attitude. The vehicle will also affect the operation accuracy of the manipulator end-effector, which brings challenges to the underwater target grasping. Therefore, the position and attitude stability control of the ARV in the floating grasp operation is very important for the successful completion of the underwater target grasp operation. This paper takes the operational full-depth autonomous and remotely-operated submersible "Haidou-1" as the research target, focusing on the stability control of the vehicle's position and attitude under floating grab operation. The main research contents and results of this paper are as follows: (1) Firstly, the kinematic and dynamic model of the vehicle and manipulator were established, respectively. The manipulator disturbance exerted on the vehicle is analyzed. Finally, the ARV system model was obtained. Then, a visual simulation environment was built in the Simscape environment. (2) According to the requirements of the stability control of the vehicle's position and attitude when the manipulator performs the floating grab operation, we proposed a composite control algorithm composed of the adaptive tracking differentiator, reduced-order extended state observer, and modified adaptive generalized super-twisting algorithm. The adaptive tracking differentiator can arrange a smooth transition process, reduce the system overshoot, and can also adapt to the step signal of different amplitude well. The linear extended state observer is developed to estimate and compensate the disturbance in real-time, and the dependence of the controller on the system model is eliminated; it can also improve the robustness of the controller. The modified adaptive generalized super-twisting algorithm uses the variable gain strategy; an upper bound of the disturbance derivative is not needed anymore. The proposed new variable gain strategy allows the gain to be reduced according to a specific rule, which effectively avoids overestimating the control gain and the oscillation of the gain near the set minimum value. Finally, the stability of the closed-loop system is proved through the Lyapunov stability theory. (3) In order to realize the engineering application of the composite control algorithm in Haidou-1, the discrete implementation of the algorithm is studied. Considering that explicit discretization of the generalized super-twisting algorithm in the controller will lead to additional discretization chattering problem, this paper not only uses the common explicit discretization method to discretize the generalized super-twisting algorithm, but also discusses the semi-implicit discretization and implicit discretization of the generalized super-twisting algorithm. (4) Based on the dynamic model and response characteristics of Haidou 1, Simulation experiments are carried out in the Simscape environment, and the parameters of the controller are obtained by combining the multi-objective optimization method and manual adjustment. The simulation results show that the proposed control scheme can control the stability of the vehicle's position and attitude well during the floating grasping process. Compared with the original adaptive generalized super-twisting algorithm, the proposed algorithm has better chattering reduction ability and control performance. The controller gain is not overestimated, nor does it oscillate near the set minimum value. In addition, the proposed control algorithm provides better chattering reduction ability after both implicit discretization and semi-implicit discretization, the controller is not sensitive to too high control gain, and parameter tuning becomes easy.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/28960
Collection水下机器人研究室
Affiliation中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
丁宁宁. 面向水下目标抓取的自主遥控水下机器人位姿稳定控制研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2021.
Files in This Item:
File Name/Size DocType Version Access License
面向水下目标抓取的自主遥控水下机器人位姿(5096KB)学位论文 开放获取CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[丁宁宁]'s Articles
Baidu academic
Similar articles in Baidu academic
[丁宁宁]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[丁宁宁]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

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