A learning trajectory planning for vibration suppression of industrial robot
ISSN: 0143-991x
Article publication date: 10 May 2023
Issue publication date: 9 August 2023
Abstract
Purpose
This paper aims to propose a learning exponential jerk trajectory planning to suppress the residual vibrations of industrial robots.
Design/methodology/approach
Based on finite impulse response filter technology, a step signal with a proper amplitude first passes through two linear filters and then performs exponential filter shaping to obtain an exponential jerk trajectory and cancel oscillation modal. An iterative learning strategy designed by gradient descent principle is used to adjust the parameters of exponential filter online and achieve the maximum vibration suppression effect.
Findings
By building a SCARA robot experiment platform, a series of contrast experiments are conducted. The results show that the proposed method can effectively suppress residual vibration compared to zero vibration shaper and zero vibration and derivative shaper.
Originality/value
The idea of the adopted iterative leaning strategy is simple and reduces the computing power of the controller. A cheap acceleration sensor is available because it just needs to measure vibration energy to feedback. Therefore, the proposed method can be applied to production practice.
Keywords
Acknowledgements
This work was supported in part by the Natural Science Foundation of Guangdong Province under Grant 2021A1515011736 and in part by the Applied basic Research Project of Guangzhou under Grant 202102080351.
Citation
Zou, Y., Liu, T., Zhang, T. and Chu, H. (2023), "A learning trajectory planning for vibration suppression of industrial robot", Industrial Robot, Vol. 50 No. 5, pp. 861-869. https://doi.org/10.1108/IR-02-2023-0013
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited