Research on integrated design method of robot intelligent joint drive and control structure

被引:0
|
作者
Mo S. [1 ,2 ]
Zhou C. [1 ]
Li X. [1 ]
Yang Z. [1 ]
Liu H. [3 ]
Gao H. [4 ]
机构
[1] School of Mechanical Engineering, Tiangong University, Tianjin
[2] Tianjin Key Laboratory of Modern Electromechanical Equipment Technology, Tianjin
[3] Dongguan Desheng Intelligent Technology Co., Ltd., Dongguan
[4] State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing
来源
关键词
Brushed DC motor; CAN bus; Double closed-loop control; Robot waist joint; Textile robot;
D O I
10.13475/j.fzxb.20210402708
中图分类号
学科分类号
摘要
In order to reduce the space occupied by textile robot and to improve its control performance, a new type of textile robot waist joint used for doffing management was designed, which used a brushed DC motor as the power source, STM32F103T8U6 as the main control chip, and L9110S as the driving chip. The magnetic encoder AS5600 and a magnet installed at the end of the output shaft was used together to generate the Hall effect to facilitate the speed and position detection of the robot joint. By comparing the corresponding speed and current of the single-speed closed-loop control with that of the double closed-loop control, this research adopted the speed and current double closed-loop proportional integral (PI) control system for speed regulation in the control algorithm, and the controller area network (CAN) bus method was used to achieve signal transmission so as to reduce the complexity of multi-joint wiring. The experimental results show that the designed robot can effectively reduce the joint volume with the miniaturization design, and has a wider range of use. Compared with the single closed-loop control system, the double closed-loop control system shortens the steady-state time by 30%. © 2022, Periodical Agency of Journal of Textile Research. All right reserved.
引用
收藏
页码:160 / 167
页数:7
相关论文
共 20 条
  • [1] TAN Min, WANG Shuo, Research progress in robotics, Acta Automatica Sinica, 39, 7, pp. 963-972, (2013)
  • [2] GAO Feng, GUO Weizhong, Thinking of the development strategy of robots in china, Journal of Mechanical Engineering, 52, 7, pp. 1-5, (2016)
  • [3] OUYANG P R, ACOB J, PANO V., PD with sliding mode control for trajectory tracking of robotic system, Robotics and Computer-Integrated Manufacturing, 30, 2, pp. 189-200, (2014)
  • [4] XIAO B, YIN S, KAYNAK O., Exponential tracking control of robotic manipulators with uncertain dynamics and kinematics, IEEE Transactions on Industrial Informatics, 63, 10, pp. 6439-6449, (2016)
  • [5] GALICKI M., Finite-time trajectory tracking control in a task space of robotic manipulators, Automatica, 67, pp. 165-170, (2016)
  • [6] YANG Sicheng, ZHANG Wenzeng, CAO Liguo, Development of a novel squirrel-cage doffing robot, Mechanical Drive, 41, 12, pp. 138-145, (2017)
  • [7] GU Wanli, HU Yunfeng, ZHANG Sen, Et al., Design and experiments on the adaptive sliding mode controller of brushed DC motor, Journal of Xi'an Jiaotong University, 51, 9, pp. 112-117, (2017)
  • [8] LI Zongli, XU Fang, LIANG Dinan, Et al., Design and implementation of constraint predictive controller for brushed DC motor, Journal of Jilin Univer-sity (Information Science), 35, 4, pp. 363-369, (2017)
  • [9] CHEN Z Y, LIU Y, HE W, Et al., Adaptive-neural-network-based trajectory tracking control for a nonholonomic wheeled mobile robot with velocity constraints, IEEE Transactions on Industrial Electronics, 68, 6, pp. 5057-5067, (2021)
  • [10] ZHAO X W, TAO B, QIAN L, Et al., Model-based actor-critic learning for optimal tracking control of robots with input saturation, IEEE Transactions on Industrial Electronics, 68, 6, pp. 5046-5056, (2021)