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Li et al. Intell Robot 2022;2(1):89–104 Intelligence & Robotics
DOI: 10.20517/ir.2022.02

Research Article Open Access

An open-closed-loop iterative learning control for tra-
jectory tracking of a high-speed 4-dof parallel robot

Qiancheng Li, Enyu Liu, Chuangchuang Cui, Guanglei Wu

School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China.

Correspondence to: Assoc. Prof./Dr. Guanglei Wu, School of Mechanical Engineering, Dalian University of Technology, No.2
Linggong Road, Ganjingzi District, Dalian 116024, Liaoning, China. E-mail: gwu@dlut.edu.cn
How to cite this article: Li Q, Liu E , Cui C, Wu G. An open-closed-loop iterative learning control for trajectory tracking of a high-
speed 4-dof parallel robot. Intell Robot 2022;2(1):89-104. http://dx.doi.org/10.20517/ir.2022.02
Received: 21 Jan 2022 First Decision: 3 Mar 2022 Revised: 10 Mar 2022 Accepted: 14 Mar 2022 Published: 31 Mar 2022

Academic Editors: Simon X. Yang, Tao Ren Copy Editor: Jia-Xin Zhang Production Editor: Jia-Xin Zhang

Abstract
Precise control is of importance for robots, whereas, due to the presence of modeling errors and uncertainties under
the complex working environment, it is difficult to obtain an accurate dynamic model of the robot, leading to decreased
control performances. This work presents an open-closed-loop iterative learning control applied to a four-limb parallel
Schönflies-motion robot, aiming to improve the tracking accuracy with high movement, in which the controller can
learn from the iterative errors to make the robot end-effector approximate to the expected trajectory. The control
algorithm is compared with classical D-ILC, which is illustrated along with an industrial trajectory of pick-and-place
operation. External repetitive and non-repetitive disturbances are added to verify the robustness of the proposed
approach. To verify the overall performance of the proposed control law, multiple trajectories within the workspace,
different working frequencies for a prescribed trajectory, and different design methods are selected, which show the
effectiveness and the generalization ability of the designed controller.

Keywords: High-speed parallel robot, open-closed-loop, iterative learning control, trajectory tracking control

1. INTRODUCTION
With the rapid development of robotic technology, robots have found their industrial applications in many
fields to replace a large amount of manpower. Among their applications, material handling is an important
[1]
aspect, in which the Delta and SCARA robots are extensively deployed . Compared to serial robots, parallel
robots have received more attention thanks to their high speed, high stiffness-to-weight ratio, and low inertia,

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, shar­
ing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you
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