基于机器视觉和改进PID的压电柔性机械臂振动控制

马天兵; 周青; 杜菲; 刘健

doi:10.3788/OPE.20202801.0141

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基于机器视觉和改进PID的压电柔性机械臂振动控制

微纳技术与精密机械 | 更新时间：2020-08-13

- 基于机器视觉和改进PID的压电柔性机械臂振动控制
- Piezoelectric flexible manipulator vibration control based on machine vision and improved PID
- 光学精密工程 2020年28卷第1期页码：141-150
- 作者机构：
  
  1.安徽理工大学深部煤矿采动响应与灾害防控国家重点实验室, 安徽淮南 232001
  2.安徽理工大学机械工程学院, 安徽淮南 232001
  3.安徽理工大学矿山智能装备与技术安徽省重点实验室, 安徽淮南 232001
- 作者简介：
  
  [ "马天兵(1981-)，男，安徽庐江人，博士，教授, 2002年于安徽理工大学获得学士学位，2005年于安徽理工大学获得硕士学位，2014年于南京航空航天大学获得博士学位，现为安徽理工大学机电控制工程系主任，主要从事压电能量回收、振动主动控制方面研究。E-mail:dfmtb@163.com" ]
  周青(1993-)，女，安徽铜陵人，硕士研究生，2017年于安徽理工大学获得学士学位，主要从事振动主动控制方面的研究。E-mail:2311355319@qq.com
  杜菲(1981-)，女，安徽舒城人，硕士，副教授，2002年于安徽理工大学获得学士学位，2008年于贵州大学获得硕士学位，主要从事微能量回收及振动控制方面研究。E-mail:453589872@qq.com DU Fei, E-mail:453589872@qq.com
- 基金信息：
  
  国家自然科学基金资助项目(51305003);安徽省博士后基金项目(2017B172);；安徽理工大学国家自然基金预研项目(2016yz004);安徽省高校自然科学研究重大项目(KJ2015ZD19)
- DOI：10.3788/OPE.20202801.0141
  中图分类号： TP273
- 收稿日期：2019-08-13，
  
  录用日期：2019-10-6，
  
  纸质出版日期：2020-01-15
- 稿件说明：
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马天兵, 周青, 杜菲, 等. 基于机器视觉和改进PID的压电柔性机械臂振动控制[J]. 光学精密工程, 2020,28(1):141-150.

Tian-bing MA, Qing ZHOU, Fei DU, et al. Piezoelectric flexible manipulator vibration control based on machine vision and improved PID[J]. Optics and precision engineering, 2020, 28(1): 141-150.
马天兵, 周青, 杜菲, 等. 基于机器视觉和改进PID的压电柔性机械臂振动控制[J]. 光学精密工程, 2020,28(1):141-150. DOI： 10.3788/OPE.20202801.0141.

Tian-bing MA, Qing ZHOU, Fei DU, et al. Piezoelectric flexible manipulator vibration control based on machine vision and improved PID[J]. Optics and precision engineering, 2020, 28(1): 141-150. DOI： 10.3788/OPE.20202801.0141.

摘要

为解决振动主动控制系统中接触式测量影响结构特性以及PID控制参数整定不理想的问题，提出利用机器视觉技术测量结构振动，并结合人工鱼群算法优化的PID进行振动控制。首先，选择刚柔双关节机械臂作为研究对象，搭建实验平台并设计正交试验，探究刚柔耦合机械臂振动情况，并确定振动控制中电机参数的设置；其次，利用CCD相机采集机械臂末端标记点振动图像，并处理得到振动位移，将其作为控制系统输入；最后，选择人工鱼群算法对PID控制器参数进行优化，控制器输出信号经输出卡转化为控制电压，再经功率放大器放大，驱动压电作动片实现振动控制。实验结果表明，相较于传统参数整定PID平均44.06%的控制效果，优化后的PID算法平均控制效果可达到57.54%

验证了基于视觉测振的优化PID控制系统的可行性和优越性。

Abstract

Considering the problem that the contact measurement influenced structure characteristics and the PID control parameter setting were not ideal in the vibration active control system

the machine vision technology was used to measure the vibration of the structure

and the vibration control was carried out with the PID optimized by artificial fish swarm algorithm. Rigid and flexible double-joint mechanical arm was selected as the research object. Firstly

the experimental platform was set up and the orthogonal test was designed to explore the vibration situation of rigid-flexible coupling mechanical arm and determine the motor setting parameters for subsequent vibration control; Secondly

CCD camera was used to capture the vibration image of the marker at the end of the mechanical arm

and the vibration displacement was obtained by processing and was used as the input of the control system. Finally

the artificial fish swarm algorithm was selected to optimize the PID parameters. The output signal of the controller was converted into control voltage by the output card. The control voltage was amplified by the power amplifier and drived the piezoelectric actuator to realize vibration control. The experimental results show that the average control effect of the optimized PID algorithm can reach 57.54% compared with the traditional PID control effect of 44.06%

which verifies the feasibility and superiority of the optimized PID control system based on visual vibration measurement.

关键词

Keywords

references

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