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Dynamic Model Identification for Adaptive Polishing System

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  • Intelligent Control and Applications
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Abstract

In this paper, a decoupled and adaptive polishing system without force sensors is designed for the industrial robot to track the rapid change of the contact force and eliminate dynamical nonlinearities in the polishing process. An identification method is proposed to obtain the dynamic model of this system. The system dynamic model is composed of the nominal linear model and the nonlinear model. The parameters of the system linear time-invariant (LTI) model is identified by frequency domain response with the disturbance observer in this paper. Regarding the high order differential terms and uncertain errors of the nonlinear part of the dynamic model, the Long-Short Term Memory (LSTM) is introduced for identifying system nonlinear characteristics. The bounds of the learning rate are discussed and the LSTM stability analysis result shows that the proposed method holds the Lyapunov stability. Finally, the experimental results show that a more accurate dynamic model can be established by combing frequency domain response and LSTM.

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Author information

Authors and Affiliations

  1. Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, 150001, China

    Hao An, Sining Zhang, Chaoran Cui, Cheng Qian & Weiyang Lin

  2. Ningbo Institute of Intelligent Equipment Technology Co., Ltd, Ningbo, 315200, China

    Weiyang Lin

Authors
  1. Hao An
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  2. Sining Zhang
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  3. Chaoran Cui
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  4. Cheng Qian
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  5. Weiyang Lin
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Corresponding author

Correspondence to Weiyang Lin.

Additional information

This work was supported in part by the National Natural Science Foundation of China (61973099).

Hao An received his Bachelor’s degree from Taiyuan University of Technology, China, in 2016, and an M.S. degree in Control and Simulation Center from Harbin Institute of Technology, China, in 2018. He is currently pusuing a Ph.D. degree with the Research Institute of Intelligence Control and Systems, Harbin Institute of Technology. His research interests include robotic dynamics, system identification, and reinforcement learning.

Sining Zhang received his Bachelor’s degree from Nan**g Normal University, Nan**g, China, in 2020. He is currently pursuing an M.Eng. degree with the Research Institute of Intelligence Control and Systems, Harbin Institute of Technology. His research interests include robot systems, compliance control, and reinforcement learning.

Chaoran Cui received his Bachelor’s degree in automation from Harbin Engineering University, China, in 2019, and an M.S. degree in control science and engineering from Harbin Institute of Technology, China, in 2021. His research interests include robotic kinematics, dynamics, and trajectory planning.

Cheng Qian received his Bachelor’s degree in mechatronics engineering from the Harbin Institute of Technology, Harbin, China, in 2014, and an M.Sc. degree in mechanical engineering from the University of Sheffield, Sheffield, UK, in 2016. He is currently pursuing a Ph.D. degree with the Research Institute of Intelligence Control and Systems, Harbin Institute of Technology. His research interests include microdevices, micro manipulation, image processing, and visual servo system.

Weiyang Lin received his Bachelor’s and M.S. degrees in mechanical engineering from Harbin Institute of Technology, China, in 2006 and 2008, respectively; and a Ph.D. degree in mechatronics engineering from Harbin Institute of Technology Shenzhen Graduate School, China, in 2014. Currently, he is an Associate Professor in the Research Institute of Intelligent Control and Systems, Harbin Institute of Technology. His research interests include parallel manipulators, robotic motion control, and medical robotic design and control.

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An, H., Zhang, S., Cui, C. et al. Dynamic Model Identification for Adaptive Polishing System. Int. J. Control Autom. Syst. 20, 3110–3120 (2022). https://doi.org/10.1007/s12555-021-0205-y

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  • DOI: https://doi.org/10.1007/s12555-021-0205-y

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