基于变增益自抗扰技术的机器人轨迹跟踪控制方法

doi:10.12263/DZXB.20200100

电子学报 ›› 2022, Vol. 50 ›› Issue (1): 89-97.DOI: 10.12263/DZXB.20200100

基于变增益自抗扰技术的机器人轨迹跟踪控制方法

张磊¹, 鲁凯², 高春侠¹, 刘荣明¹, 丁浩¹, 田伟¹, 洪伟¹

^1.中国石油大学（华东）新能源学院，山东青岛 266580
^2.中海石油（中国）有限公司天津分公司，天津 300459

收稿日期:2020-01-15 修回日期:2020-11-10 出版日期:2022-01-25
- 作者简介:
- 张磊男，1977年12月出生，河北任丘人.现为中国石油大学（华东）新能源学院教授，硕士生导师.主要研究方向为机器人轨迹跟踪控制、电力电子与电气传动. E-mail:zl@s.upc.edu.cn
  鲁凯男，1994年5月出生，河南西平人.2020年毕业于中国石油大学（华东）新能源学院，获硕士学位.主要研究方向为机器人轨迹跟踪控制. E-mail:z17055012@s.upc.edu.cn
- 基金资助:
- 国家自然科学基金重点项目 (60534020); 山东省优秀中青年科学家科研奖励基金 (BS2010DX038)

Path Tracking Control Method of Robot Based on Time-Varying Gain Active Disturbance Rejection Control

ZHANG Lei¹, LU Kai², GAO Chun-xia¹, LIU Rong-ming¹, DING Hao¹, TIAN Wei¹, HONG Wei¹

^1.College of New Energy, China University of Petroleum （East China）, Qingdao, Shandong 266580, China
^2.Tianjin Branch of China National Offshore Oil Corporation, Tianjin 300459, China

Received:2020-01-15 Revised:2020-11-10 Online:2022-01-25 Published:2022-01-25
- Supported by:
- Key Program of National Natural Science Foundation of China (60534020); Scientific Research Award Fund for Outstanding Young and Middle-aged Scientists of Shandong Province (BS2010DX038)

摘要/Abstract

摘要：

本文针对机器人轨迹跟踪控制精度和初始峰值问题，提出一种变增益自抗扰控制器轨迹跟踪控制方法.首先，采用跟踪微分器将逆运动学解处理为插值点区间，设定插值点选取规则及冲击力目标函数.然后，根据目标函数选取插值点拟合得到的轨迹曲线.最后，在考虑机器人动力学下设计轨迹跟踪控制器.仿真和实验结果表明，优化的方法冲击力减少39.8%，且电机能量消耗降低28.48%.设计的变增益自抗扰控制器轨迹跟踪控制可以准确地跟踪轨迹以及解决峰值问题.

关键词: 关节机器人, 轨迹规划, 轨迹跟踪控制, 自抗扰技术

Abstract:

Aiming at the problem of robot trajectory tracking control accuracy and initial peak value, this paper proposes a variable gain active disturbance rejection controller trajectory tracking control method. First, the tracking differentiator is used to process the inverse kinematics solution into an interpolation point interval, set interpolation point selection rule and impact force objective function. Then, according to the objective function, the interpolation points are selected to fit the obtained trajectory curve. Finally, the trajectory tracking controller is designed considering the robot dynamics. Simulation and experimental results show that the optimized method reduces the impact force by 39.8%, and the energy consumption of the motor is reduced by 28.48%. The designed variable gain active disturbance rejection controller trajectory tracking control can accurately track the trajectory and solve the peak problem.

Key words: joint robot, trajectory planning, trajectory tracking control, active disturbance rejection control

中图分类号:

TM249

张磊, 鲁凯, 高春侠, 等. 基于变增益自抗扰技术的机器人轨迹跟踪控制方法[J]. 电子学报, 2022, 50(1): 89-97.

Lei ZHANG, Kai LU, Chun-xia GAO, et al. Path Tracking Control Method of Robot Based on Time-Varying Gain Active Disturbance Rejection Control[J]. Acta Electronica Sinica, 2022, 50(1): 89-97.

图/表 18

图1 二自由度机器人简化模型

图2 3阶ADRC控制器结构

表1 二自由度机器人约束条件

约束	J₁	J₂
v_c（°/s）	45	40
a_c（°/s²）	30	25
j_c（°/s³）	60	55

图3 关节变量跟踪值

表2 关节变量插值点数据

时间（s）	关节2角度（ $°$ ）	关节1角度（ $°$ ）
0	15	-5
0~1.5	20	0
1.5~2	30	10
2~2.5	45	22.2
2.5~3	60.2	32.4
3~3.5	72.8	38.8
3.5~5	81.3	42.1
5	90	45

表2 关节变量插值点数据

时间（s）	关节2角度（ $°$ ）	关节1角度（ $°$ ）
0	15	-5
0~1.5	20	0
1.5~2	30	10
2~2.5	45	22.2
2.5~3	60.2	32.4
3~3.5	72.8	38.8
3.5~5	81.3	42.1
5	90	45

图4 关节2轨迹曲线

图5 关节2速度曲线

图6 关节2加速度曲线

图7 关节2加加速度曲线

图8 ESO时变参数

图9 关节轨迹曲线

图10 关节控制变量

图11 关节1电机能量消耗

图12 实验环境

图13 常规控制轨迹跟踪曲线

图14 优化控制轨迹跟踪曲线

图15 关节1常规轨迹动态响应

图16 关节1优化轨迹动态响应

参考文献 21

1	宛敏红, 周维佳, 骆海涛, 等. 高精度重载搅拌摩擦焊机器人设计与运动控制[J]. 机器人, 2018, 40(6): 817-834.
	WANM H, ZHOUW J, LUOH T, et al.Design and motion control of the high precision heavy load friction stir welding robot [J]. Robot, 2018, 40(6): 817-834. (in Chinese)
2	HEG Z. Inverse kinematics of 2R positioner based on welding postion representation[J].Chinese Journal of Mechanical Engineering,2006,42(6): 86-91.
3	LIM H, HONGB R, LUOR H. Mobile robot simultaneous localization and mapping using novel rao-blackwellised particle filter[J]. Chinese Journal of Electronics, 2007, 16(1): 34-39.
4	王乐乐, 眭泽智, 蒲志强, 等. 一种改进RRT的多机器人编队路径规划算法[J]. 电子学报, 2020, 48(11): 2138-2145.
	WANGL L, SUIZ Z, PUZ Q, et al. An improved RRT algorithm for multi-robot formation path planning[J]. Acta Electronica Sinica, 2020, 48(11): 2138-2145. (in Chinese)
5	Ruchanurucks. Humanoid robot upper body motion generation using B-spline-based functions[J].Robotica, 2015, 33(4): 705-720.
6	许凯波, 鲁海燕, 黄洋, 等. 基于双层蚁群算法和动态环境的机器人路径规划方法[J]. 电子学报, 2019, 47(10): 2166-2176.
	XUK B, LUH Y, HUANGY, et al. Robot path planning based on double-layer ant colony optimization algorithm and dynamic environment[J]. Acta Electronica Sinica, 2019, 47(10): 2166-2176. (in Chinese)
7	BARBIET, KABUTANR, TANAKAR, et al. Gaussi an mixture spline trajectory: learning from a dataset, generating trajectories without one[J]. Advanced Robotics, 2018, 32(10):547-558.
8	阮久宏, 李贻斌, 杨福广, 等. 高速4WID-4WIS自主车路径跟踪控制[J]. 机器人, 2011, 33(4): 411-418.
	RUANJ H, LIY B, YANGF G, et al. Path tracking control of high-speed 4WID-4WIS autonomous vehicle[J]. Robot, 2011, 33(4): 411-418. (in Chinese)
9	曹政才, 赵应涛, 付宜利. 车式移动机器人轨迹跟踪控制方法[J]. 电子学报, 2012, 40(4): 632-635.
	CAOZ C, ZHAOY T, FUY L. Trajectory tracking control approach of car-like mobile robot[J]. Acta Electronica Sinica, 2012, 40(4): 632-635. (in Chinese)
10	杨亮, 付根平, 陈勇. 考虑关节角加速度约束的仿人机器人偏摆力矩控制方法[J]. 电子学报, 2019, 47(2): 475-482.
	YANGL, FUG P, CHENY. Yaw moment control for humanoid robot with joint acceleration constraints[J]. Acta Electronica Sinica, 2019, 47(2): 475-482. (in Chinese)
11	刘国荣, 张扬名. 移动机器人轨迹跟踪的模糊PID-P型迭代学习控制[J]. 电子学报, 2013, 41(8): 1536-1541.
	LIUG R, ZHANGY M. Trajectory tracking of mobile robots based on fuzzy PID-P type iterative learning control [J]. Acta Electronica Sinica, 2013, 41(8): 1536-1541. (in Chinese)
12	MAKAROVM, GROSSARDM, RODRIGUEZ-AYERBEP, et al. Modeling and preview H∞ control design for motion control of elastic-joint robots with uncertainties[J]. IEEE Transactions on Industrial Electronics, 2016, 63(10):6429-6438.
13	LIUH, LAIX, WUW. Time-optimal and jerk continuous trajectory planning for robot manipulators with kinematic constraints[J]. Robotics and Computer Integrated Manufacturing, 2013, 29(2): 309-317.
14	李壮举, 梅杰, 曹少中, 等. 多信息融合精密装配机器人建模与控制方法研究[J]. 电子学报, 2018, 46(3): 636-640.
	LIZ J, MEIJ, CAOS Z, et al. Modeling and control of automatic precision assembly robot with multi-information fusion measuring system and control algorithm[J]. Acta Electronica Sinica, 2018, 46(3): 636-640. (in Chinese)
15	尹伟, 孙雷, 王萌, 等. 用于柔性关节机器人位置控制的幅值饱和非线性状态反馈控制器[J]. 机器人, 2017(4) 2017, 39(4): 458-465.
	YINW, SUNL, WANGM, et al. Amplitude-saturated nonlinear state feedback position control for flexible joint robots[J]. Robot, 2017, 39(4): 458-465. (in Chinese)
16	TAHAC. Smooth point-to-point trajectory planning for robot manipulators by using radial basis functions[J]. Robotica, 2019, 37(3): 539-559.
17	韩京清. 自抗扰控制技术: 估计补偿不确定因素的控制技术[M]. 北京: 国防工业出版社, 2008: 46-56.
	HANJ Q. Active Disturbance Rejection Control Technique: Technique for Estimating and Compensating The Uncertainties[M].Beijing,China: National Defense Industry Press, 2008:46-56. (in Chinese)
18	胡小平, 彭涛, 左富勇. 一种基于多项式和Newton插值法的机械手轨迹规划方法[J]. 中国机械工程, 2012, 23(24): 2946-2949.
	HUX P, PENGT, ZUOF Y. A trajectory planning method based on Newton interpolation and polynomial for manipulator[J]. China Mechanical Engineering, 2012, 23(24): 2946-2949. (in Chinese)
19	徐海黎, 解祥荣, 庄健, 等.工业机器人的最优时间与最优能量轨迹规划[J].机械工程学报, 2010, 46(9): 19-25.
	XUH L, XIEX R, ZHUANGJ, et al. Global time-energy optimal planning of industrial robot trajectories[J]. Journal of Mechanical Engineering,2010, 46(9): 19-25. (in Chinese)
20	PUZ Q, YUANR Y, YIJ Q, et al. A class of adaptive extended state observers for nonlinear disturbed systems[J]. IEEE Transactions on Industrial Electronics, 62(9): 5858-5869.
21	邵星灵, 王宏伦. 基于改进sigmoid函数的非线性跟踪微分器[J]. 控制理论与应用, 2014, 31(8): 1116-1122.
	SHAOX L, WANGH L. Nolinear tracking differentiator based on improved sigmoid function[J]. Control Theory & Applications, 2014, 31(8): 1116-1122. (in Chinese)

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基于变增益自抗扰技术的机器人轨迹跟踪控制方法

Path Tracking Control Method of Robot Based on Time-Varying Gain Active Disturbance Rejection Control

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