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工程设计学报
Chin J Eng Design  2022, Vol. 29 Issue (5): 564-571    DOI: 10.3785/j.issn.1006-754X.2022.00.073
Design for Quality     
Research on trajectory tracking control of metallurgical industrial robot based on fuzzy iterative Q-learning
Hui ZHANG1,2(),Yong-fei ZHU1,Xue-fei LIU1,Xiang-rong XU1()
1.School of Mechanical Engineering, Anhui University of Technology, Ma’anshan, 243032, China
2.School of Metallurgical Engineering, Anhui University of Technology, Ma’anshan, 243032, China
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Abstract  

Metallurgical industrial robots play an increasingly irreplaceable role in modern industrial production. Due to the great improvement of industrial automation, people constantly put forward new requirements for the performance of metallurgical industrial robots, especially for the stability of them control system. In view of the problems of low trajectory tracking accuracy and lack of adaptive dynamic adjustment characteristics of metallurgical industrial robots, a fuzzy iterative Q-learning control algorithm was proposed. Taking 6-DOF (six-degree-of-freedom) dual-arm robot as the research object, the fuzzy control rules were compiled by using the Fuzzy toolbox and the position error and its change rate generated by the robot were taken as the input of the fuzzy controller, and then the quantization factor and scale factor in the fuzzy controller and the PD (proportional derivative) parameter in the iterative learning control was adjusted by introducing the Q-learning strategy, so as to complete the design of fuzzy iterative Q-learning controller. Then, combined with ADAMS (automatic dynamic analysis of mechanical systems) and MATLAB software, a 6-DOF dual-arm robot simulation platform was built to carry out the simulation of shaft hole assembly task with high-precision trajectory tracking. The simulation results showed that the 6-DOF dual-arm robot had high trajectory tracking accuracy in joint space, and could complete the coordinated assembly task of dual arm shaft holes, which verified the effectiveness and advancement of the proposed control algorithm. The research results can provide reference for the shaft hole assembly of dual-arm cooperative robot with high-precision trajectory tracking, and have certain practical application value.

Key words： dual-arm robot      fuzzy control      iterative Q-learning control      trajectory tracking      shaft hole assembly     
Received: 14 February 2022      Published: 02 November 2022
CLC:  TP 242.2  
Corresponding Authors: Xiang-rong XU     E-mail: 3181@ahut.edu.cn;xuxr@ahut.edu.cn
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Hui ZHANG
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Cite this article:

Hui ZHANG,Yong-fei ZHU,Xue-fei LIU,Xiang-rong XU. Research on trajectory tracking control of metallurgical industrial robot based on fuzzy iterative Q-learning. Chin J Eng Design, 2022, 29(5): 564-571.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2022.00.073     OR     https://www.zjujournals.com/gcsjxb/Y2022/V29/I5/564


基于模糊迭代Q-学习的冶金工业机器人轨迹跟踪控制研究

冶金工业机器人在现代工业生产中扮演着越来越不可替代的角色。由于工业自动化程度的大幅提升，人们对冶金工业机器人的性能也不断地提出新要求，尤其是对其控制系统的稳定性提出了更高的要求。针对目前冶金工业机器人轨迹跟踪精度较低且不具有自适应动态调节特性等问题，提出了一种模糊迭代Q-学习控制算法。以6-DOF（six degree-of-freedom，六自由度）双臂机器人为研究对象，利用Fuzzy工具箱编写模糊控制规则，以机器人产生的位置误差以及位置误差的变化率为模糊控制器的输入量，并引入Q-学习策略，以调整模糊控制器中的量化因子、比例因子以及迭代学习控制中的PD（proportional derivative，比例微分）参数，完成模糊迭代Q-学习控制器的设计。然后，联合ADAMS（automatic dynamic analysis of mechanical systems，机械系统动力学自动分析）和MATLAB软件搭建6-DOF双臂机器人仿真平台，开展高精度轨迹跟踪的轴孔装配任务模拟。仿真结果表明，6-DOF双臂机器人关节空间的轨迹跟踪精度较高，同时可以完成双臂轴孔协调装配任务，验证了所提出控制算法的有效性和先进性。研究结果可为双臂协作机器人实现高精度轨迹跟踪的轴孔装配提供参考，具有一定的实际应用价值。


关键词： 双臂机器人,  模糊控制,  迭代Q-学习控制,  轨迹跟踪,  轴孔装配 
Fig.1 Structure block diagram of fuzzy controller
Fig.2 Structure of fuzzy Q-learning controller
Fig.3 Structure of fuzzy iterative Q-learning controller
关节iθi /radαi-1/raddi /mmai-1/mm
1θ1000
2θ2-π/2d20
3θ3π/200
4θ4-π/2d40
5θ5π/200
6θ6-π/2d60
Table 1 D-H parameters of left mechanical arm
Fig.4 Simulation model of 6-DOF dual-arm robot
关节质量/kg质心坐标/10-3m转动惯量/(kg·m2)
IxxIyyIzz
159.3(0.496, -10.5, -61.44)7.005.601.910
24.18(0.496, 527, 233)0.0460.0410.030
33.29(0.496, 700, 233)0.0390.0330.025
47.67(0.496, 920, 233)0.0650.0630.035
53.29(0.496, 1 140 233)0.0390.0330.025
67.67(0.496, 1 340, 233)0.0650.0630.035
Table 2 Dynamic parameters of left mechanical arm
Fig.5 Mechanical arm control subsystem module
Fig.6 Input and output variable setting of mechanical arm control subsystem module
Fig.7 Trajectory tracking results of each joint of left mechanical arm
Fig.8 Shaft hole assembly process of 6-DOF dual-arm robot
Fig.9 Dynamic characteristics of each joint of left mechanical arm during shaft hole assembly
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