Skip to main content
SpringerLink
Log in

An adaptive impedance control method for blade polishing based on the Kalman filter

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Robotic force control is crucial for precise polishing and has a significant influence on the final effects. The blade has a free-form surface in space, and the curvature changes drastically, making traditional impedance control feedback untimely. To solve this problem, this paper proposes an adaptive impedance control method for blade polishing based on Kalman filter. The force data is denoised by Kalman filtering to obtain the real force data, then the data is gravity compensated to obtain the real polishing force. The method analyzes the influences of stiffness change and displacement change on the polishing force, and establishes a stiffness and displacement coupling compensation model. The method achieves timely feedback when the robot copes with unknown environmental stiffness changes. In addition, the Lyapunov function is applied to verify the stability of the method during implementation. Four processing conditions are simulated by using Matlab Simulink. The results indicate that the proposed method can provide faster response and higher force tracking accuracy by adjusting the reference position when the environment changes. In the experiment of polishing blade, the roughness is reduced to below Ra0.32 μm and fluctuation range of polishing force is within ±1 N. The force control method performance is significantly improved and the blade surface quality is effectively improved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33
Fig. 34
Fig. 35
Fig. 36
Fig. 37

Similar content being viewed by others

Data availability

All data generated or analyzed during this study are included in this published paper.

Code availability

Not applicable.

References

  1. Wang W, Liu F, Liu Z, Yun C (2017) Prediction of depth of cut for robotic belt grinding. Int J Adv Manuf Technol 91:699–708. https://doi.org/10.1007/s00170-016-9729-3

    Article  Google Scholar 

  2. Wang H, Zhu D, Liu J (2019) Improving the accuracy of the blade leading/trailing edges by electrochemical machining with tangential feeding. CIRP Ann 68(1):165–168

    Article  Google Scholar 

  3. Jin L, Zhang Y, Li S, Zhang Y (2016) Modified ZNN for time-varying quadratic programming with inherent tolerance to noises and its application to kinematic redundancy resolution of robot manipulators. IEEE Trans Ind Electron 63(11):6978–6988. https://doi.org/10.1109/TIE.2016.2590379

    Article  Google Scholar 

  4. Zhang J, Liu J, Yang S (2022) Trajectory planning of robot-assisted abrasive cloth wheel polishing blade based on flexible contact. Int J Adv Manuf Technol 119:8211–8225. https://doi.org/10.1007/s00170-022-08737-9

    Article  Google Scholar 

  5. Xie, Q., Zhao, H., Wang, T., & Ding, H. (2019). Adaptive impedance control for robotic polishing with an intelligent digital compliant grinder. In Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part VI 12 (pp. 482-494). Springer International Publishing. https://doi.org/10.1007/978-3-030-27529-7_41

  6. Hogan, N. (1984). Impedance control: an approach to manipulation. In 1984 American Control Conference (pp. 304-313). IEEE. https://doi.org/10.23919/acc.1984.4788393

  7. Dinh TX, Thien TD, Anh THV, Ahn KK (2018) Disturbance observer based finite time trajectory tracking control for a 3 DOF hydraulic manipulator including actuator dynamics. IEEE Access 6:36798–36809. https://doi.org/10.1109/ACCESS.2018.2848240

    Article  Google Scholar 

  8. Xue X, Huang H, Zuo L, Wang N (2022) A compliant force control scheme for industrial robot interactive operation. Front Neurorobot 16. https://doi.org/10.3389/fnbot.2022.865187

  9. Duan J, Gan Y, Chen M, Dai X (2019) Symmetrical adaptive variable admittance control for position/force tracking of dual-arm cooperative manipulators with unknown trajectory deviations. Robot Comput-Integr Manuf 57:357–369. https://doi.org/10.1016/j.rcim.2018.12.012

    Article  Google Scholar 

  10. Wen S, Chen J, Qin G, Zhu Q, Wang H (2018) An improved fuzzy model predictive control algorithm based on the force/position control structure of the five-degree of freedom redundant actuation parallel robot. Int J Adv Robot Syst 15(5). https://doi.org/10.1177/1729881418804979

  11. Tuan DM, Hieu PD (2019) Adaptive position/force control for robot manipulators using force and velocity observer. J Electr Eng Technol 14:2575–2582. https://doi.org/10.1007/s42835-019-00281-z

    Article  Google Scholar 

  12. Zhou P, Zhou Y, Xie Q, Zhao H (2019) Adaptive force control for robotic grinding of complex blades. IOP Conference Series: Materials Science and Engineering 692(1). https://doi.org/10.1088/1757-899X/692/1/012008

  13. Ochoa H, Cortesão R (2022) Impedance control architecture for robotic-assisted mold polishing based on human demonstration. IEEE Trans Ind Electron 69(4):3822–3830. https://doi.org/10.1109/TIE.2021.3073310

    Article  Google Scholar 

  14. Dong J, Shi J, Liu C, Yu T (2021) Research of pneumatic polishing force control system based on high speed on/off with PWM controlling. Robot Comput Integr Manuf 70. https://doi.org/10.1016/j.rcim.2021.102133

  15. Dong Y, Ren T, Hu K et al (2020) Contact force detection and control for robotic polishing based on joint torque sensors. Int J Adv Manuf Technol 107:2745–2756. https://doi.org/10.1007/s00170-020-05162-8

    Article  Google Scholar 

  16. Li M, Du Z, Dong W, Gao K, Gao Y, Wu D (2022) Modeling, planning, and control of robotic grinding on free-form surface using a force-controlled belt grinding tool. Proc Inst Mech Eng C J Mech Eng Sci 236(4):2009–2028. https://doi.org/10.1177/0954406220931529

    Article  Google Scholar 

  17. Chen F, Zhao H, Li D, Chen L, Tan C, Ding H (2019) Robotic grinding of a blisk with two degrees of freedom contact force control. Int J Adv Manuf Technol 101(1–4):461–474. https://doi.org/10.1007/s00170-018-2925-6

    Article  Google Scholar 

  18. Zhou H, Ma S, Wang G, Deng Y, Liu Z (2021) A hybrid control strategy for grinding and polishing robot based on adaptive impedance control. Advances in Mechanical Engineering 13(3). https://doi.org/10.1177/16878140211004034

  19. Wang Z, Zou L, Duan L, Liu X, Lv C, Gong M, Huang Y (2021) Study on passive compliance control in robotic belt grinding of nickel-based superalloy blade. J Manuf Process 68:168–179. https://doi.org/10.1016/j.jmapro.2021.07.020

    Article  Google Scholar 

  20. Dedong T, Fengxiao L, Jingang J, Shichang S, Yang Z (2023) A review on end-effectors of robotic grinding. Recent Patents on. Engineering 17(1):e220322202521. https://doi.org/10.2174/1872212116666220322142201

    Article  Google Scholar 

  21. Burghardt A, Szybicki D, Kurc K, Muszyńska M (2022) Robotic grinding process of turboprop engine compressor blades with active selection of contact force. Tehnički vjesnik 29(1):15–22. https://doi.org/10.17559/TV-20190710141137

    Article  Google Scholar 

  22. Chen BH, Wang YH, Lin PC (2019) A feedback force controller fusing traditional control and reinforcement learning strategies. In: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, pp 259–265. https://doi.org/10.1109/AIM.2019.8868711

    Chapter  Google Scholar 

  23. S. Chen, Z. Wang, A. Chakraborty, M. Klecka, G. Saunders and J. Wen. Robotic deep rolling with iterative learning motion and force control. IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5581-5588, Oct. 2020, doi: https://doi.org/10.1109/LRA.2020.3009076.

  24. Stouraitis T, Chatzinikolaidis I, Gienger M, Vijayakumar S (2020) Online hybrid motion planning for dyadic collaborative manipulation via bilevel optimization. IEEE Trans Robot 36(5):1452–1471. https://doi.org/10.1109/TRO.2020.2992987

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 52105474 and Grant No. 52375460) and The Shanxi Scholarship Council of China.

Author information

Authors and Affiliations

  1. Shanxi Province Key Laboratory of Precise Machining, School of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan, 030024, China

    Xuhui Zhao, Jia Liu, Shengqiang Yang, Jingjing Zhang, Xufeng Lv, Long Cheng & Xueqian Zhang

Authors
  1. Xuhui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shengqiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingjing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xufeng Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Long Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xueqian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The proposal and realization of this technology were mainly completed by Xuhui Zhao. Jia Liu provided machine tools and experimental conditions and participated in technical discussions. Shengqiang Yang provided guidance on research directions. Jingjing Zhang, Xufeng Lv, Long Cheng, and Xueqian Zhang participated in the research.

Corresponding authors

Correspondence to Jia Liu or Shengqiang Yang.

Ethics declarations

Conflict of interest

Jia Liu has received research support from the funding. Xuhui Zhao, Shengqiang Yang, Jingjing Zhang, Xufeng Lv, Long Cheng, and Xueqian Zhang did not get paid from the funding.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, X., Liu, J., Yang, S. et al. An adaptive impedance control method for blade polishing based on the Kalman filter. Int J Adv Manuf Technol 132, 1723–1739 (2024). https://doi.org/10.1007/s00170-024-13401-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-024-13401-5

Keywords

Search

Navigation