Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

被引:8
作者
Hu, Hongjun [1 ,2 ]
Xiao, Shungen [1 ]
Shen, Haikuo [2 ]
机构
[1] Ningde Normal Univ, Sch Informat Mech & Elect Engn, Ningde 352100, Peoples R China
[2] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing 100044, Peoples R China
关键词
ITERATIVE LEARNING CONTROL;
D O I
10.1155/2021/8892032
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
To solve the problems of model uncertainties, dynamic coupling, and external disturbances, a modified linear active disturbance rejection controller (MLADRC) is proposed for the trajectory tracking control of robot manipulators. In the computer simulation, MLADRC is compared to the proportional-derivative (PD) controller and the regular linear active disturbance rejection controller (LADRC) for performance tests. Multiple uncertain factors such as friction, parameter perturbation, and external disturbance are sequentially added to the system to simulate an actual robot manipulator system. Besides, a two-degree-of-freedom (2-DOF) manipulator is constructed to verify the control performance of the MLADRC. Compared with the regular LADRC, MLADRC is significantly characterized by the addition of feedforward control of reference angular acceleration, which helps robot manipulators keep up with target trajectories more accurately. The simulation and experimental results demonstrate the superiority of the MLADRC over the regular LADRC for the trajectory tracking control.
引用
收藏
页数:13
相关论文
共 40 条
[31]   Trajectory tracking of wheeled mobile robot by adopting iterative learning control with predictive, current, and past learning items [J].
Yu, Chong ;
Chen, Xiong .
ROBOTICA, 2015, 33 (07) :1393-1414
[32]   An Enhanced Adaptive Time Delay Control-Based Integral Sliding Mode for Trajectory Tracking of Robot Manipulators [J].
Boudjedir, Chems Eddine ;
Bouri, Mohamed ;
Boukhetala, Djamel .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2023, 31 (03) :1042-1050
[33]   High order iterative learning control to solve the trajectory tracking problem for robot manipulators using Lyapunov theory [J].
Bouakrif, Farah ;
Zasadzinski, Michel .
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2018, 40 (15) :4105-4114
[34]   Segment-wise learning control for trajectory tracking of robot manipulators under iteration-dependent periods [J].
Zhang, Fan ;
Meng, Deyuan ;
Cai, Kaiquan .
SCIENCE CHINA-INFORMATION SCIENCES, 2024, 67 (03)
[35]   Trajectory tracking control of a manipulator based on an immune algorithm-optimized neural network in the presence of unknown backlash-like hysteresis [J].
Chen, Jiqing ;
Zhang, Haiyan ;
Zhu, Tongtong ;
Pan, Shangtao .
APPLIED MATHEMATICS AND COMPUTATION, 2024, 470
[36]   Data-driven iterative learning trajectory tracking control for wheeled mobile robot under constraint of velocity saturation [J].
Bu, Xiaodong ;
Dai, Xisheng ;
Hou, Rui .
IET CYBER-SYSTEMS AND ROBOTICS, 2023, 5 (02)
[37]   Research on Trajectory Tracking Control of Non-Singular Fast Terminal Sliding Mode Iterative Learning for Robot Manipulators [J].
Chen, Tao ;
Li, Xiaojuan ;
Liu, Jianxuan ;
Wang, Lizhong .
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 2025, 59 (01) :125-135and147
[38]   Iterative learning control for non-repetitive trajectory tracking of robot manipulators with joint position constraints and actuator faults [J].
Jin, Xu .
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2017, 31 (06) :859-875
[39]   Non-linear model predictive control based trajectory tracking of hand and wrist motion using functional electrical stimulation [J].
Karak, Tarun ;
Basak, Souvik ;
Joseph, Patrick A. ;
Sengupta, Somnath .
CONTROL ENGINEERING PRACTICE, 2024, 146
[40]   High-Order Internal Model-Based Iterative Learning Control for 2-D Linear FMMI Systems With Iteration-Varying Trajectory Tracking [J].
Wan, Kai ;
Li, Xiao-dong .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (03) :1462-1472