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Adaptive Variable Impedance Control with Fuzzy-PI Compound Controller for Robot Trimming System

  • Research Article-Mechanical Engineering
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Abstract

The supporting element is an important part of the robot trimming system and is used to locate and support thin-walled parts. However, the unstable supporting force directly leads to vibration and even deformation of thin-walled parts, so maintaining an ideal supporting force is essential to raise the processing quality of thin-walled parts. In this paper, an adaptive variable impedance control with Fuzzy-PI compound controller designed for the supporting element is presented, which has the ability to resolve the force tracking problem under unknown environments. The method combines the fast response of Fuzzy control with the steady-state error suppression of PI control and can quickly and stably track the ideal force. In this study, the defects of the traditional constant impedance control in an unknown environment are firstly pointed out, and then an adaptive variable impedance control method using a Fuzzy-PI compound controller to adjust the target damping online is proposed to compensate for force tracking errors caused by the unknown environment based on these defects. In addition, the stability and convergence of the proposed method in the force tracking process are demonstrated. The simulation and experimental results show that, compared to the traditional constant impedance control, the force tracking performance of the proposed control method is significantly improved in terms of response speed and steady-state accuracy.

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References

  1. Cao, H.; He, Y.; Chen, X.; Liu, Z.: Control of adaptive switching in the sensing-executing mode used to mitigate collision in robot force control. J. Dyn. Syst., Meas., Control. 141(11), 111003 (2019)

  2. Cao, H.; Chen, X.; He, Y.; Zhao, X.: Dynamic adaptive hybrid impedance control for dynamic contact force tracking in uncertain environments. IEEE Access. 7, 83162–83174 (2019)

    Article  Google Scholar 

  3. Liu, X.; Ge, S.S.; Zhao, F.; Mei, X.: Optimized impedance adaptation of robot manipulator interacting with unknown environment. IEEE Trans. Control Syst. Technol. 29(1), 411–419 (2021)

    Article  Google Scholar 

  4. Raibert, M.H.; Craig, J.J.: Hybrid position/force control of manipulators. J. Dyn. Syst., Meas., Control. 103(2), 126–133 (1981)

  5. Rani, M.; Kumar, N.: A New hybrid position/force control scheme for coordinated multiple mobile manipulators. Arab. J. Sci. Eng. 44, 2399–2411 (2019)

    Article  Google Scholar 

  6. Singh, H.P.; Sukavanam, N.: Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties. Neural Comput. Appl. 22(7–8), 1745–1755 (2013)

    Article  Google Scholar 

  7. Hogan, N.: Impedance Control: An Approach to Manipulation. 1984 American Control Conference. pp. 304–313 (1984). Doi: https://doi.org/10.23919/ACC.1984.4788393

  8. Tufail, M.; Anwar, S.; Khan, Z.A.; Khan, M.T.: Real-time impedance control based on learned inverse dynamics. Arab. J. Sci. Eng. 45, 5043–5055 (2020)

    Article  Google Scholar 

  9. Jung, S.; Hsia, T.C.; Bonitz, R.G.: Force tracking impedance control of robot manipulators under unknown environment. IEEE Trans. Control Syst. Technol. 12(3), 474–483 (2004)

    Article  Google Scholar 

  10. Baigzadehnoe, B.; Rahmani, Z.; Khosravi, A.; Rezaie, B.: On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach. ISA Trans. 70, 432–446 (2017)

    Article  Google Scholar 

  11. Ito, S.; Darainy, M.; Sasaki, M.; Ostry, D.J.: Computational model of motor learning and perceptual change. Biol. Cybern. 107(6), 653–667 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhang, X.; Khamesee, M.B.: Adaptive force tracking control of a magnetically navigated microrobot in uncertain environment. IEEE/ASME Trans. Mechatronics. 22(4), 1644–1651 (2017)

    Article  Google Scholar 

  13. Kong, X.; Ba, K.; Yu, B.; Cao, Y.; Zhu, Q.; Zhao, H.: Force control compensation method with variable load stiffness and damping of the hydraulic drive unit force control system. Chin. J. Mech. Eng. 29(3), 454–464 (2016)

    Article  Google Scholar 

  14. He, G.; Fan, Y.; Su, T.; Zhao, L.; Zhao, Q.: Variable impedance control of cable actuated continuum manipulators. Int. J. Control Autom. Syst. 18(7), 1839–1852 (2020)

    Article  Google Scholar 

  15. Ficuciello, F.; Villani, L.; Siciliano, B.: Variable impedance control of redundant manipulators for intuitive human-robot physical interaction. IEEE Trans. Robot. 31(4), 850–863 (2015)

    Article  Google Scholar 

  16. Zhang, X.; Sun, T.; Deng, D.: Neural approximation-based adaptive variable impedance control of robots. Trans. Inst. Meas. Control. 42(13), 2589–2598 (2020)

    Article  Google Scholar 

  17. Xu, Q.; Sun, X.: Adaptive impedance control of robots with reference trajectory learning. IEEE Access. 8, 104967–104976 (2020)

    Article  Google Scholar 

  18. Li, Y.; Ganesh, G.; Jarrassé, N.; Haddadin, S.; Albu-Schaeffer, A.; Burdet, E.: Force, impedance, and trajectory learning for contact tooling and haptic identification. IEEE Trans. Robot. 34(5), 1170–1182 (2018)

    Article  Google Scholar 

  19. Roveda, L.; Iannacci, N.; Vicentini, F.; Pedrocchi, N.; Braghin, F.; Tosatti, L.M.: Optimal impedance force-tracking control design with impact formulation for Interaction tasks. IEEE Robot. Autom. Lett. 1(1), 130–136 (2016)

    Article  Google Scholar 

  20. Seraji, H.; Colbaugh, R.: Force tracking in impedance control. Int. J. Robot. Res. 16(1), 97–117 (1997)

    Article  Google Scholar 

  21. Li, C.; Zhang, Z.; Xia, G.; Xie, X.; Zhu, Q.: Efficient force control learning system for industrial robots based on variable impedance control. Sensors. 18(8), 2539 (2018)

    Article  Google Scholar 

  22. Duan, J.; Gan, Y.; Chen, M.; Dai, X.: Adaptive variable impedance control for dynamic contact force tracking in uncertain environment. Robot. Auton. Syst. 102, 54–65 (2018)

    Article  Google Scholar 

  23. Xu, K., ;Wang, S.; Yue, B.; Wang, J.; Peng, H.; Liu, D.; Chen, Z.; Shi, M.: Adaptive impedance control with variable target stiffness for wheel-legged robot on complex unknown terrain. Mechatronics. 69, 102388 (2020)

  24. Li, Y.; Ge, S.S.: Impedance learning for robots interacting with unknown environments. IEEE Trans. Control Syst. Technol. 22(4), 1422–1432 (2014)

    Article  Google Scholar 

  25. Mao, D.; Yang, W.; Du, Z.: Fuzzy variable impedance control based on stiffness identification for human-robot cooperation. IPO Conf., Earth Environ. Sci. 69, 012090 (2017). Doi :https://doi.org/10.1088/1755-1315/69/1/012090

  26. Tsuji, T., ;Terauchi, M.; Tanaka, Y.: Online learning of virtual impedance parameters in non-contact impedance control using neural networks. IEEE Trans. Syst., Man, Cybern. B. 34(5), 2112–2118 (2004)

  27. Hamedani, M.H.; Zekri, M.; Sheikholeslam, F.; Selvaggio, M.; Ficuciello, F.; Siciliano, B.: Recurrent fuzzy wavelet neural network variable impedance control of robotic manipulators with fuzzy gain dynamic surface in an unknown varied environment. Fuzzy Sets Syst. 416, 1–26 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  28. Fereydooni, R.H.; Siahkali, H.; Shayanfar, H.A.; Mazinan, A.H.: sEMG-based variable impedance control of lower-limb rehabilitation robot using wavelet neural network and model reference adaptive control. Ind. Robot. 47(3), 349–358 (2020)

    Article  Google Scholar 

  29. Zhang, X.; Sun, L.; Kuang, Z.; Tomizuka, M.: Learning variable impedance control via inverse reinforcement learning for force-related tasks. IEEE Robot. Autom. Lett. 6(2), 2225–2232 (2021)

    Article  Google Scholar 

  30. Roveda, L.; Maskani, J.; Franceschi, P.; Abdi, A.; Braghin, F.; Tosatti, L.M.; Pedrocchi, N.: Model-based reinforcement learning variable impedance control for human-robot collaboration. J. Intell. Robot. Syst. 100, 417–433 (2020)

    Article  Google Scholar 

  31. Ji, W., ;Zhang, J.; Xu, B.; Tang, C.; Zhao, D.: Grasping mode analysis and adaptive impedance control for apple harvesting robotic grippers. Comput. Electron Agric. 186, 106210 (2021)

  32. Deng, Z.; Jin, H.; Hu, Y.; He, Y.; Zhang, P.; Tian, W.; Zhang, J.: Fuzzy force control and state detection in vertebral lamina milling. Mechatronics 35, 1–10 (2016)

    Article  Google Scholar 

  33. Karar, M.E.: A simulation study of adaptive force controller for medical robotic liver ultrasound guidance. Arab. J. Sci. Eng. 43, 4229–4238 (2018)

    Article  Google Scholar 

  34. Du, H., ;Sun, Y.; Feng, D.; Xu, J.: Automatic robotic polishing on titanium alloy parts with compliant force/position control. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 229(7), 1180–1192 (2015)

  35. Lilly, J.H.: Fuzzy Control and Identification. Wiley. (2010)

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Acknowledgements

The authors would like to acknowledge Yangzi Automobile Interior Parts Co., Ltd. (Yangzhou, Jiangsu, China.) for support of this work.

Funding

This study was funded by Jiangsu Natural Science Foundation of China(No.BK20190473).

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Authors and Affiliations

  1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Ze Liu & Yu Sun

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  1. Ze Liu
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  2. Yu Sun
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Contributions

The first author ZL has been responsible for developing the force control method, designing the simplified supporting experiments, and writing this paper. YS has offered great help to the revision of this paper and the analysis of theory.

Corresponding author

Correspondence to Yu Sun.

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Liu, Z., Sun, Y. Adaptive Variable Impedance Control with Fuzzy-PI Compound Controller for Robot Trimming System. Arab J Sci Eng 47, 15727–15740 (2022). https://doi.org/10.1007/s13369-022-06755-z

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  • DOI: https://doi.org/10.1007/s13369-022-06755-z

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