Skip to main content
SpringerLink
Log in

An iterative learning method for realizing accurate dynamic feedforward control of an industrial hybrid robot

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Feedforward control based on an accurate dynamic model is an effective approach to reduce the dynamic effect of the robot and improve its performance. However, due to the complicated work environment with considerable uncertainty, it is difficult to obtain a high-precision dynamic model of the robot, which severely deteriorates the achievable control performance. This paper proposes an iterative learning method to accurately design the industrial feedforward controller and compensate for the external uncertain dynamic load of the robot. Based on a standard dynamic model, a complete linear feedforward controller is presented. An iterative design strategy is given to iteratively update the feedforward controller by combining the Moore-Penrose Inverse and the PID learning rate. Experiments are carried out on a 5-DOF industrial hybrid robot to validate the effectiveness of the proposed iterative learning method. The experiment results illustrate that the industrial feedforward controller can rapidly converge to the optimal controller and significantly improve the servo performance by using the proposed method. This paper provides an effective method for applying iterative learning control to an unopened industrial control system. It is very useful for the practical control of hybrid robots in industrial field.

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

Similar content being viewed by others

References

  1. Huang T, Wang M, Yang S, et al. Force/motion transmissibility analysis of six degree of freedom parallel mechanisms. J Mech Robot, 2014, 6: 031010

    Article  Google Scholar 

  2. Kang X, Dai J S. Relevance and transferability for parallel mechanisms with reconfigurable platforms. J Mech Robot, 2019, 11: 031012

    Article  Google Scholar 

  3. Wu J, Wang J, Wang L, et al. Dynamics and control of a planar 3-DOF parallel manipulator with actuation redundancy. Mech Mach Theor, 2009, 44: 835–849

    Article  Google Scholar 

  4. Li Z Y, Zhao D J, Zhao J S. Structure synthesis and workspace analysis of a telescopic spraying robot. Mech Mach Theor, 2019, 133: 295–310

    Article  Google Scholar 

  5. Zhao Y, Gao F. Dynamic performance comparison of the 8PSS redundant parallel manipulator and its non-redundant counterpart—The 6PSS parallel manipulator. Mech Mach Theor, 2009, 44: 991–1008

    Article  Google Scholar 

  6. Rushworth A, Axinte D, Raffles M, et al. A concept for actuating and controlling a leg of a novel walking parallel kinematic machine tool. Mechatronics, 2016, 40: 63–77

    Article  Google Scholar 

  7. Lu Y, Dai Z. Dynamics model of redundant hybrid manipulators connected in series by three or more different parallel manipulators with linear active legs. Mech Mach Theor, 2016, 103: 222–235

    Article  Google Scholar 

  8. Wu J, Yu G, Gao Y, et al. Mechatronics modeling and vibration analysis of a 2-DOF parallel manipulator in a 5-DOF hybrid machine tool. Mech Mach Theor, 2018, 121: 430–445

    Article  Google Scholar 

  9. Shang W, Cong S. Nonlinear computed torque control for a highspeed planar parallel manipulator. Mechatronics, 2009, 19: 987–992

    Article  Google Scholar 

  10. Yang X, Liu H, Xiao J, et al. Continuous friction feedforward sliding mode controller for a TriMule hybrid robot. IEEE/ASME Trans Mechatron, 2018, 23: 1673–1683

    Article  Google Scholar 

  11. Matsubara A, Nagaoka K, Fujita T. Model-reference feedforward controller design for high-accuracy contouring control of machine tool axes. CIRP Ann, 2011, 60: 415–418

    Article  Google Scholar 

  12. Veronesi M, Visioli A. Automatic tuning of feedforward controllers for disturbance rejection. Ind Eng Chem Res, 2014, 53: 2764–2770

    Article  Google Scholar 

  13. Lipiński K. Modeling and control of a redundantly actuated variable mass 3RRR planar manipulator controlled by a model-based feedforward and a model-based-proportional-derivative feedforward-feedback controller. Mechatronics, 2016, 37: 42–53

    Article  Google Scholar 

  14. Liu H, Huang T, Chetwynd D G, et al. Stiffness modeling of parallel mechanisms at limb and joint/link levels. IEEE Trans Robot, 2017, 33: 734–741

    Article  Google Scholar 

  15. Wu J, Wang J, You Z. An overview of dynamic parameter identification of robots. Robot Com-Int Manuf, 2010, 26: 414–419

    Article  Google Scholar 

  16. Shang W, Cong S, Kong F. Identification of dynamic and friction parameters of a parallel manipulator with actuation redundancy. Mechatronics, 2010, 20: 192–200

    Article  Google Scholar 

  17. Bukkems B, Kostic D, de Jager B, et al. Learning-based identification and iterative learning control of direct-drive robots. IEEE Trans Contr Syst Technol, 2005, 13: 537–549

    Article  Google Scholar 

  18. Wu J, Han Y, Xiong Z, et al. Servo performance improvement through iterative tuning feedforward controller with disturbance compensator. Int J Mach Tools Manuf, 2017, 117: 1–10

    Article  Google Scholar 

  19. Wang Z, Hu C, Zhu Y, et al. Newton-ILC contouring error estimation and coordinated motion control for precision multiaxis systems with comparative experiments. IEEE Trans Ind Electron, 2017, 65: 1470–1480

    Article  Google Scholar 

  20. Liu Q, Xiao J, Yang X, et al. An iterative tuning approach for feedforward control of parallel manipulators by considering joint couplings. Mech Mach Theor, 2019, 140: 159–169

    Article  Google Scholar 

  21. Li M, Zhu Y, Yang K, et al. Convergence rate oriented iterative feedback tuning with application to an ultraprecision wafer stage. IEEE Trans Ind Electron, 2018, 66: 1993–2003

    Article  Google Scholar 

  22. Kober J, Bagnell J A, Peters J. Reinforcement learning in robotics: A survey. Int J Robot Res, 2013, 32: 1238–1274

    Article  Google Scholar 

  23. Rani P, Liu C, Sarkar N, et al. An empirical study of machine learning techniques for affect recognition in human-robot interaction. Pattern Anal Applic, 2006, 9: 58–69

    Article  Google Scholar 

  24. Pérez D, Quintana Y. A survey on the Weierstrass approximation theory. Divulgac Matemat, 2008, 16: 231–247

    MATH  Google Scholar 

  25. Park K H. A study on the robustness of a PID-type iterative learning controller against initial state error. Int J Syst Sci, 1999, 30: 49–59

    Article  Google Scholar 

  26. Zhang B, Wu J, Wang L, et al. Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot. Robot Com-Int Manuf, 2020, 63: 101923

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Tribology and Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Jun Wu, BinBin Zhang, LiPing Wang & Guang Yu

  2. Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipment and Control, Beijing, 100084, China

    Jun Wu, BinBin Zhang, LiPing Wang & Guang Yu

Authors
  1. Jun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. BinBin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. LiPing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Wu.

Additional information

This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFE0111300), EU H2020-MSCA-RISE-ECSASDPE (Grant No. 734272), and the National Natural Science Foundation of China (Grant No. 51975321).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, J., Zhang, B., Wang, L. et al. An iterative learning method for realizing accurate dynamic feedforward control of an industrial hybrid robot. Sci. China Technol. Sci. 64, 1177–1188 (2021). https://doi.org/10.1007/s11431-020-1738-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-020-1738-5

Keywords

Search

Navigation