[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

A three-loop physical parameter identification method of robot manipulators considering physical feasibility and nonlinear friction model

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This paper proposed a three-loop physical parameter identification method considering physical feasibility and nonlinear friction model. The full physical parameters can be obtained with physical feasibility by constructing an optimization problem. And the nonlinear friction model which considered Stribeck effect is employed to improve identification accuracy. In the first loop, the physical parameters are identified with a regression model. In the second loop, the nonlinear friction model is identified with a nonlinear optimization method. And in the third loop, the obtained friction parameters are updated and the identification results are to be further optimized. Different from traditional methods like the least squares (LS), weight least squares (WLS) and other optimization methods which can only get base parameters and do not consider Stribeck effect, the proposed scheme can get physical parameters with physical constraints. It is useful in many robotic applications, like model-based control. The Stribeck effect is also employed to improve identification accuracy. The experimental results verified the effectiveness of the proposed method.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Algorithm 1
Algorithm 2
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability Statement

Not applicable.

References

  1. Dai, L., Yuantao, Y., Di-Hua, Z., Huang, T., Yuanqing, X.: Robust model predictive tracking control for robot manipulators with disturbances. IEEE Trans. Ind. Electron 68(5), 4288–4297 (2021)

    Article  Google Scholar 

  2. Danni, S., Jinhui, Z., Zhongqi, S., Ganghui, S., Yuanqing, X.: Composite trajectory tracking control for robot manipulator with active disturbance rejection. Control Eng. Practice. 106, 104670 (2021)

  3. Zhuang, L., Yue, Z., et al.: A novel faster fixed-time adaptive control for robotic systems with input saturation. May. IEEE Trans. Ind. Electron. 7(5), 5215–5223 (2024)

    Google Scholar 

  4. Sun, C., Wang, S., Yu, H.: Finite-time sliding mode control based on unknown system dynamics estimator for nonlinear robotic systems. IEEE Trans. Circuits Syst. II-Express Briefs. 70(7), 2535–2539 (2023)

    Google Scholar 

  5. Huayang, S., Zhenbang, X., et al.: Approximate continuous fixed-time terminal sliding mode control with prescribed performance for uncertain robotic manipulators. Sep. Nonlinear Dyn. 110(1), 431–448 (2022)

    Article  Google Scholar 

  6. Lachner, J., Allmendinger, F., Stramigioli, S., Hogan, N.: Shaping impedances to comply with constrained task dynamics. IEEE Trans. Robot. 38(5), 2750–2767 (2022)

    Article  Google Scholar 

  7. Zhehao, J., Dongdong, Q., Andong, L., Wen-An, Z., Yu, L.: Model predictive variable impedance control of manipulators for adaptive precision-compliance tradeoff. IEEE-ASME Trans. Mechatron. 28(2), 1174–1186 (2023)

    Article  Google Scholar 

  8. Sharifi, M., Behzadipour, S., Vossoughi, G.: Nonlinear model reference adaptive impedance control for human-robot interactions. Control Eng. Practice. Nov. 32, 9–27 (2014)

    Article  Google Scholar 

  9. Xingwei, Z., Shibo, H., et al.: Model-based actor-critic learning of robotic impedance control in complex interactive environment. IEEE Trans. Ind. Electron. Dec. 69(12), 13225–13235 (2022)

    Article  Google Scholar 

  10. Liang, J., Chen, Y., Lai, N., He, B., Miao, Z., Wang, Y.: Low-complexity prescribed performance control for unmanned aerial manipulator robot system under model uncertainty and unknown disturbances. IEEE Trans. Ind. Inform. 18(7), 4632–4641 (2022)

    Article  Google Scholar 

  11. Jun, W., Jinsong, W., Zheng, Y.: An overview of dynamic parameter identification of robots. Robot. Comput.-Integr. Manuf. Oct. 26(5), 414–419 (2010)

    Article  Google Scholar 

  12. Argin, O.F., Bayraktaroglu, Z.Y.: Consistent dynamic model identification of the Staubli RX-160 industrial robot using convex optimization method. J. Mech. Sci. Technol. May. 35(5), 2185–2195 (2021)

    Article  Google Scholar 

  13. Deng, J., Weiwei, S., Bin, Z., Shengchao, Z., Shuang, C.: Dynamic model identification of collaborative robots using a three-loop iterative method. In: 2021 6th IEEE international conference on advanced robotics and mechatronics (ICARM). pp. 937-942 (2021)

  14. Claudio, G., Cognetti, M., Oliva, A., Robuffo Giordano, P., De Luca, A.: Dynamic identification of the franka emika panda robot with retrieval of feasible parameters using penalty-based optimization. IEEE Robot. Autom. Lett. 4(4), 4147–4154 (2019)

    Article  Google Scholar 

  15. Shi-Ping, L., Zi-Yan, M., et al.: An improved parameter identification method of redundant manipulator. Int. J. Adv. Robot. Syst. Mar. 18(2), 17298814211002118 (2021)

    Google Scholar 

  16. Urrea, C., Pascal, J.: Design and validation of a dynamic parameter identification model for industrial manipulator robots. Arch. Appl. Mech. May. 91(5), 1981–2007 (2021)

    Article  Google Scholar 

  17. Zhou, Y., Zhongcan, L., et al.: A semilinearized approach for dynamic identification of manipulator based on nonlinear friction model. IEEE Trans. Instrum. Meas. (2024). https://doi.org/10.1109/TIM.2024.3374292

    Article  Google Scholar 

  18. Golluccio, G., Gillini, G., et al.: Robot dynamics identification. IEEE Robot. Autom. Mag. 28(3), 128–140 (2021)

    Article  Google Scholar 

  19. Dawoon, J., Joono, C., et al.: Backward sequential approach for dynamic parameter identification of robot manipulators. Int. J. Adv. Robot. Syst. 15(1), 1729881418758578 (2018)

    Google Scholar 

  20. Minan, T., Yaguang, Y., An, B., Wenjuan, W., Yaqi, Z.: Dynamic parameter identification of collaborative robot based on WLS-RWPSO algorithm. Machines. 11(2), 316 (2023)

    Article  Google Scholar 

  21. Urrea, C., Agramonte, R.: Evaluation of parameter identification of a real manipulator robot. Symmetry-Basel. 14(7), 1446 (2022)

    Article  Google Scholar 

  22. Chao, T., Huan, Z., Han, D.: Non-redundant inertial parameters determination for dynamic identification of branched articulated robots. Ind. Robot. 49(6), 1229–1241 (2022)

    Article  Google Scholar 

  23. Minan, T., Yaguang, Y., et al.: Dynamic parameter identification of collaborative robot based on WLS-RWPSO algorithm. Machines. 11(2), 316 (2023)

    Article  Google Scholar 

  24. Khalil, W., Creusot, D.: SYMORO+: A system for the symbolic modelling of robots. Robotica. 15(2), 153–161 (1997)

    Article  Google Scholar 

  25. Khalil, W., Vijayalingam, A., et al.: OpenSYMORO: an open-source software package for symbolic modelling of robots. In: 2014 IEEE/ASME international conference on advanced intelligent mechatronics, Besacon, France. pp. 1206–1211 (2014)

  26. Bethge, S., Malzahn, J., Tsagarakis, N., Caldwell, D.: FloBaRoID - a software package for the identification of robot dynamics parameters. In: Advances in service and industrial robotics: mechanisms and machine science, 26th international conference on robotics in alpe-adria-danube region (RAAD), Tech Univ Politecnico Torino, Turin, Italy, Jun 21-23. 49, 156–165 (2018)

  27. Gautier, M., Briot, S.: Dynamic parameter identification of a 6 dof industrial robot using power model. In: IEEE international conference on robotics and automation (ICRA), Karlsruhe, Germany, May 06-10. pp. 2914–2920 (2013)

  28. Gautier, M., Janot, A., Vandanjon, P.: A new closed-loop output error method for parameter identification of robot dynamics. IEEE Trans. Control Syst. Technol. 21(2), 428–444 (2013)

    Article  Google Scholar 

  29. Karahan, O., Karci, H.: Swarm intelligence based nonlinear friction and dynamic parameters identification for a 6-DOF robotic manipulator. J. Intell. Robot. Syst. 108(2), 19 (2023)

    Article  Google Scholar 

  30. Sousa, C.D., Cortesao, R.: Physical feasibility of robot base inertial parameter identification: a linear matrix inequality approach. Int. J. Robot. Res. 33(6), 931–944 (2014)

    Article  Google Scholar 

  31. Mata, V., Benimeli, F., et al.: Dynamic parameter identification in industrial robots considering physical feasibility. Adv. Robot. 19(1), 101–119 (2005)

    Article  Google Scholar 

  32. Sousa, C.D., Cortesao, R.: Inertia tensor properties in robot dynamics identification: a linear matrix inequality approach. IEEE-ASME Trans. Mechatron. 24(1), 406–411 (2019)

    Article  Google Scholar 

  33. W, P.M., Sangbae, K., Slotine, J.-J.E.: Linear matrix inequalities for physically consistent inertial parameter identification: a statistical perspective on the mass distribution. IEEE Robot. Autom. Lett. 3(1), 60–67 (2018)

    Article  Google Scholar 

  34. Yong, H., Jianhua, W., Chao, L., Zhenhua, X.: An iterative approach for accurate dynamic model identification of industrial robots. IEEE Trans. Robot. 36(5), 1577–1594 (2020)

    Article  Google Scholar 

  35. Yanjiang, H., Jianhong, K., Xianmin, Z., Jun, O.: Dynamic parameter identification of serial robots using a hybrid approach. IEEE Trans. Robot. 39(2), 1607–1621 (2023)

    Article  Google Scholar 

  36. Felis, M.L.: RBDL: an efficient rigid-body dynamics library using recursive algorithms. Auton. Robot. 41(2), 495–511 (2016)

    Article  Google Scholar 

  37. Carpentier, J., Saurel, G., Buondonno, G., Mirabel, J., Lamiraux, F., Stasse, O., Mansard, N.: The Pinocchio C++ library: a fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives. In: 2019 IEEE/SICE International Symposium on System Integration (SII). pp. 614–619 (2019)

  38. Yvonne R, S., Lukas M, A., Roy S, S.: Parameter identification of the KUKA LBR iiwa robot including constraints on physical feasibility. In: Ifac papersonline, 20th world congress of the international-federation-of-automatic-control (IFAC), Toulouse, France, Jul 09-14. 50(1), 6863–6868 (2017)

  39. Fu, Z., Pan, J., Spyrakos-Papastavridis, E., Lin, Y.H., Zhou, X., Chen, X., Dai, J.S.: A lie-theory-based dynamic parameter identification methodology for serial manipulators. IEEE-ASME Trans. Mechatron 26(5), 2688–2699 (2021)

    Article  Google Scholar 

  40. Kumar, R.: Double internal loop higher-order recurrent neural network-based adaptive control of the nonlinear dynamical system. Soft Comput. 27(22), 17313–17331 (2023)

    Article  Google Scholar 

  41. Kumar, R.: Memory recurrent elman neural network-based identification of time-delayed nonlinear dynamical system. IEEE Trans. Syst. Man Cybern. -Syst. 53(2), 753–762 (2023)

    Article  Google Scholar 

  42. Kumar, R., Srivastava, S., et al.: Modeling and adaptive control of nonlinear dynamical systems using radial basis function network. Soft Comput. 21(15), 4447–4463 (2017)

    Article  Google Scholar 

  43. Gupta, T., Kumar, R.: A novel feed-through Elman neural network for predicting the compressive and flexural strengths of eco-friendly jarosite mixed concrete: design, simulation and a comparative study. Soft Comput. 28(1), 399–414 (2023)

    Article  Google Scholar 

  44. Kumar, R., Srivastava, S., et al.: Self-recurrent wavelet neural network-based identification and adaptive predictive control of nonlinear dynamical systems. Soft Comput. 32(9), 1326–1358 (2018)

  45. Kumar, R., Srivastava, S.: Externally recurrent neural network based identification of dynamic systems using lyapunov stability analysis. ISA Trans. 98, 292–308 (2020)

    Article  Google Scholar 

  46. Kumar, R., Srivastava, S., et al.: Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks. Neural Comput. Appl. 30(1), 223–239 (2018)

    Article  MathSciNet  Google Scholar 

  47. Craig, J.J.: Introduction to robotics: mechanics and control. Pearson Education Inc, London (1986)

    Google Scholar 

  48. De Luca, A., Ferrajoli, L.: A modified newton-euler method for dynamic computations in robot fault detection and control, In: 2009 IEEE international conference on robotics and automation, Kobe, Japan. pp. 3359–3364 (2009)

  49. Guanghui, L., Qiang, L., Lijin, F., Bing, H., Hualiang, Z.: A new joint friction model for parameter identification and sensor-less hand guiding in industrial robots. Ind. Robot. 47(6), 847–857 (2022)

    Google Scholar 

  50. Jingfu, J., Nicholas, G.: Parameter identification for industrial robots with a fast and robust trajectory design approach. Robot. Comput.-Integr. Manuf. 31, 21–29 (2015)

    Article  Google Scholar 

  51. Steven G, J.: The NLopt nonlinear-optimization package. https://github.com/stevengj/nlopt. (2007)

  52. Interior point optimizer. https://coin-or.github.io/Ipopt. (2015)

  53. Gurobi solver. http://www.gurobi.com. (008)

Download references

Acknowledgements

This work was supported by The Liaoning Province Basic Research Program under Grant 2022JH2/101300202; The National Natural Science Foundation of China under Grant 62273081.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lijin Fang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Song, T., Fang, L., Liu, G. et al. A three-loop physical parameter identification method of robot manipulators considering physical feasibility and nonlinear friction model. Nonlinear Dyn 112, 13115–13129 (2024). https://doi.org/10.1007/s11071-024-09755-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-024-09755-w

Keywords