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ADRC Based Multi-task Priority Tracking Control for Collaborative Robots

Fan, Kun; Liu, Yanhong; Zhang, Kuan; Bian, Guibin; Yu, Hongnian


Kun Fan

Yanhong Liu

Kuan Zhang

Guibin Bian


When collaborative robots perform multiple tracking tasks at the same time, the dynamics of each task will interact with each other. In addition, the uncertainty of robot model and external perturbation also limit the performance of the system. In order to tackle above problems, a task priority control framework combining ADRC and null space projection is proposed. First, by using the null-space projection, the new state variables which can realize the decoupling of task space inertia are obtained and the task space dynamics is inertially decoupled correspondingly. Then, by establishing the relationship between the new task state variables and the original task state variables, the task space dynamics represented by the original task state variables is got. Thirdly, based on the task space dynamics of each task, a ADRC controller is constructed to improve the tracking performance and disturbance attenuation property simultaneously. Simulation experiments on the UR5 robot verify the effectiveness of the proposed controller.

Presentation Conference Type Conference Paper (Published)
Conference Name CICAI 2022
Start Date Aug 27, 2022
End Date Aug 28, 2022
Online Publication Date Dec 17, 2022
Publication Date 2022
Deposit Date Feb 24, 2023
Publisher Springer
Pages 439-450
Series Title Lecture Notes in Computer Science
Series Number 13606
Book Title Artificial Intelligence: Second CAAI International Conference, CICAI 2022, Beijing, China, August 27–28, 2022, Revised Selected Papers, Part III
ISBN 978-3-031-20502-6
Keywords Multi-task, Tracking control, Null space projection, Inertial decoupling of task space dynamics, ADRC