Skip to main content
SpringerLink
Log in

A Practical Model-Based Robust Control for the Modular Joint of Collaborative Robots

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In this paper, in order to enhance the trajectory tracking effect of collaborative robot joint modules, a practical model-based robust control method with simple parameter adjustment is proposed. Firstly, in order to keep the nominal mechanical system stable, a nominal controller is established using the dynamics model. Secondly, a robust controller is established using the Lyapunov method to limit the impact of uncertainties on dynamic performance. Through theoretical analysis, it is proved that the uniform boundedness and uniform ultimate boundedness of the system are ensured by the controller. In addition, based on the actual experimental equipment, a prototype of rapid controller CSPACE is designed, which can quickly repeat the experiment and greatly enhance the experimental efficiency. Finally, the effectiveness and realizability of the controller are verified by virtual simulation and experimental results.

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. Zhao, J., Yan, S., Wu, J.: Analysis of parameter sensitivity of space manipulator with harmonic drive based on the revised response surface method. Acta Astronaut. 98, 86–96 (2014). https://doi.org/10.1016/j.actaastro.2014.01.017

    Article  Google Scholar 

  2. Ping, Z., Li, Y., Song, Y., Huang, Y., Wang, H., Lu, J.G.: Nonlinear speed tracking control of pmsm servo system: A global robust output regulation approach. Control Eng. Pract. 112, 104832 (2021). https://doi.org/10.1016/j.conengprac.2021.104832

    Article  Google Scholar 

  3. Corless, M.: Control of Uncertain Nonlinear Systems. J. Dyn. Syst. Meas. Control 115(2B), 362–372 (1993). https://arxiv.org/abs/https://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/115/2B/362/5546667/3621.pdf. https://doi.org/10.1115/1.2899076

  4. Ciabattoni, L., Corradini, M.L., Grisostomi, M., Ippoliti, G., Longhi, S., Orlando, G.: In: Vaidyanathan, S., Volos, C. (eds.) Variable Structure Sensorless Control of PMSM Drives, pp. 505–530. Springer, Cham (2016)

  5. Ang, K.H., Chong, G., Li, Y.: Pid control system analysis, design, and technology. IEEE Trans. Control Syst. Technol. 13(4), 559–576 (2005). https://doi.org/10.1109/TCST.2005.847331

    Article  Google Scholar 

  6. Soriano, L.A., Zamora, E., Vazquez-Nicolas, J.M., Hernández, G., Balderas, D.: Pd control compensation based on a cascade neural network applied to a robot manipulator. Front. Neurorobotics 14, 577749 (2020). https://doi.org/10.3389/fnbot.2020.577749

    Article  Google Scholar 

  7. Silva-Ortigoza, R., Hernández-Márquez, E., Roldán-Caballero, A., Tavera-Mosqueda, S., Marciano-Melchor, M., García-Sánchez, J.R., Hernández-Guzmán, V.M., Silva-Ortigoza, G.: Sensorless tracking control for a “full-bridge buck inverter-dc motor” system: Passivity and flatnessbased design. IEEE Access 9, 132191–132204 (2021). https://doi.org/10.1109/ACCESS.2021.3112575

  8. Hu, C., Jing, H., Wang, R., Yan, F., Chadli, M.: Robust H\(_{\infty }\) outputfeedback control for path following of autonomous ground vehicles. Mech. Syst. Signal Process. 70–71, 414–427 (2016). https://doi.org/10.1016/j.ymssp.2015.09.017

    Article  Google Scholar 

  9. Rubaai, A., Ofoli, A.R., Cobbinah, D.: Dsp-based real-time implementation of a hybrid \(h{\infty }\) adaptive fuzzy tracking controller for servo-motor drives. IEEE Trans. Ind. Appl. 43(2), 476–484 (2007). https://doi.org/10.1109/TIA.2006.889904

    Article  Google Scholar 

  10. Ko, P.J., Tsai, M.C.: H\(_{\infty }\) control design of pid-like controller for speed drive systems. IEEE Access 6, 36711–36722 (2018). https://doi.org/10.1109/ACCESS.2018.2851284

    Article  Google Scholar 

  11. Lughofer, E., Skrjanc, I.: Evolving error feedback fuzzy model for improved robustness under measurement noise. IEEE Trans. Fuzzy Syst. 31(3), 997–1008 (2023). https://doi.org/10.1109/TFUZZ.2022.3193451

    Article  Google Scholar 

  12. Soltanpour, M.R., Zaare, S., Haghgoo, M., Moattari, M.: Free-chattering fuzzy sliding mode control of robot manipulators with joints flexibility in presence of matched and mismatched uncertainties in model dynamic and actuators. J. Intell. Robot. Syst. 100, 47–69 (2020). https://doi.org/10.1007/s10846-020-01178-0

    Article  Google Scholar 

  13. Meda-Campaña, J.A., A. Rodríguez-Manzanarez, R., Ontiveros-Paredes, S.D., Rubio, J.d.J., Tapia-Herrera, R., Hernández-Cortés, T., Obregón- Pulido, G., Aguilar-Ibá nez, C.: An algebraic fuzzy pole placement approach to stabilize nonlinear mechanical systems. IEEE Trans. Fuzzy Syst. 30(8), 3322–3332 (2022). https://doi.org/10.1109/TFUZZ.2021.3113560

  14. Fallaha, C.J., Saad, M., Kanaan, H.Y., Al-Haddad, K.: Sliding-mode robot control with exponential reaching law. IEEE Trans. Ind. Electron. 58(2), 600–610 (2011). https://doi.org/10.1109/TIE.2010.2045995

    Article  Google Scholar 

  15. Balcazar, R., Rubio, J.d.J., Orozco, E., Andres Cordova, D., Ochoa, G., Garcia, E., Pacheco, J., Gutierrez, G.J., Mujica-Vargas, D., Aguilar-Ibañez, C.: The regulation of an electric oven and an inverted pendulum. Symmetry 14(4) (2022). https://doi.org/10.3390/sym14040759

  16. Rubio, J.D.J., Orozco, E., Cordova, D.A., Islas, M.A., Pacheco, J., Gutierrez, G.J., Zacarias, A., Soriano, L.A., Meda-Campaña, J.A., Mujica-Vargas, D.: Modified linear technique for the controllability and observability of robotic arms. IEEE Access 10, 3366–3377 (2022). https://doi.org/10.1109/ACCESS.2021.3140160

    Article  Google Scholar 

  17. Nikdel, N., Badamchizadeh, M.A., Azimirad, V., Nazari, M.A.: Adaptive backstepping control for an n-degree of freedom robotic manipulator based on combined state augmentation. Robot. Comput. Integr. Manuf. 44, 129–143 (2017). https://doi.org/10.1016/j.rcim.2016.08.007

    Article  Google Scholar 

  18. Wu, X., Jin, P., Zou, T., Qi, Z., Xiao, H., Lou, P.: Backstepping trajectory tracking based on fuzzy sliding mode control for differential mobile robots. J. Intell. Robot. Syst. 96, 109–121 (2019). https://doi.org/10.1007/s10846-019-00980-9

  19. Son, N.N., Kien, C.V., Anh, H.P.H.: A novel adaptive feed-forwardpid controller of a scara parallel robot using pneumatic artificial muscle actuator based on neural network and modified differential evolution algorithm. Rob. Auton. Syst. 96, 65–80 (2017). https://doi.org/10.1016/j.robot.2017.06.012

    Article  Google Scholar 

  20. Tzafestas, S.: Robot adaptive and robust control. J. Intell. Robot. Syst. 20, 87–91 (1997). https://doi.org/10.1023/A:1007945911032

    Article  Google Scholar 

  21. Anderson, R.B., Marshall, J.A., L’Afflitto, A., Dotterweich, J.M.: Model reference adaptive control of switched dynamical systems with applications to aerial robotics. J. Intell. Robot. Syst. 100, 1265–1281 (2020). https://doi.org/10.1007/s10846-020-01260-7

    Article  Google Scholar 

  22. Zhao, X., Fu, D.: Adaptive neural network nonsingular fast terminal sliding mode control for permanent magnet linear synchronous motor. IEEE Access 7, 180361–180372 (2019). https://doi.org/10.1109/ACCESS.2019.2958569

    Article  Google Scholar 

  23. Chen, J., Yao, W., Ren, Y., Wang, R., Zhang, L., Jiang, L.: Nonlinear adaptive speed control of a permanent magnet synchronous motor: A perturbation estimation approach. Control Eng. Pract. 85, 163–175 (2019). https://doi.org/10.1016/j.conengprac.2019.01.019

    Article  Google Scholar 

  24. Cho, C.N., Hong, J.T., Kim, H.J.: Neural network based adaptive actuator fault detection algorithm for robot manipulators. J. Intell. Robot. Syst. 95, 137–147 (2019). https://doi.org/10.1007/s10846-018-0781-0

    Article  Google Scholar 

  25. Morris, A.S., Khemaissia, S.: A neural network based adaptive robot controller. J. Intell. Robot. Syst. 15, 3–10 (1996). https://doi.org/10.1007/BF00435721

    Article  Google Scholar 

  26. Chen, S.Y., Liu, T.S.: Intelligent tracking control of a PMLSM using self-evolving probabilistic fuzzy neural network. IET Electr. Power Appl. 11(6, SI), 1043–1054 (2017). https://doi.org/10.1049/iet-epa.2016.0819

    Article  Google Scholar 

  27. Jenhani, S., Gritli, H., Carbone, P.G.: Comparison between some nonlinear controllers for the position control of lagrangian-type robotic systems. Chaos Theor. Appl. 4(4), 179–196 (2022). https://doi.org/10.51537/chaos.1184952

    Article  Google Scholar 

  28. Pennestrì, E., Rossi, V., Salvini, P., Valentini, P.P.: Review and comparison of dry friction force models. Nonlinear Dyn. 83(4), 1785–1801 (2016). https://doi.org/10.1007/s11071-015-2485-3

    Article  MATH  Google Scholar 

  29. Marques, F., Flores, P., Claro, J.P., Lankarani, H.M.: A survey and comparison of several friction force models for dynamic analysis of multibody mechanical systems. Nonlinear Dyn. 86(3), 1407–1443 (2016). https://doi.org/10.1007/s11071-016-2999-3

    Article  MathSciNet  Google Scholar 

  30. Slotine, J.J.E., Li, W.: In: Prentice-Hall (ed.) Applied Nonlinear Control, New Jersey, NJ, USA (1991)

  31. Chen, Y.H.: On the deterministic performance of uncertain dynamical systems. Int. J. Control 43(5), 1557–1579 (1986) https://arxiv.org/abs/https://doi.org/10.1080/00207178608933559. https://doi.org/10.1080/00207178608933559

  32. Gritli, H., Belghith, S.: Lmi-based synthesis of a robust saturated controller for an underactuated mechanical system subject to motion constraints. Eur. J. Control 57, 179–193 (2021). https://doi.org/10.1016/j.ejcon.2020.04.004

    Article  MathSciNet  MATH  Google Scholar 

  33. Gritli, H., Belghith, S.: Robust feedback control of the underactuated inertia wheel inverted pendulum under parametric uncertainties and subject to external disturbances: Lmi formulation. J. Franklin Inst. 355(18), 9150–9191 (2018). https://doi.org/10.1016/j.jfranklin.2017.01.035. Special Issue on Control and Signal Processing in Mechatronic Systems

  34. Antonio-Cruz, M., Hernandez-Guzman, V.M., Merlo-Zapata, C.A., Marquez-Sanchez, C.: Nonlinear control with friction compensation to swing-up a furuta pendulum. ISA Transactions (2023). https://doi.org/10.1016/j.isatra.2023.05.007

    Article  Google Scholar 

  35. Acuña-Bravo, W., Molano-Jiménez, A.G., Canuto, E.: Embedded model control for underactuated systems: An application to furuta pendulum. Control Eng. Pract. 113, 104854 (2021). https://doi.org/10.1016/j.conengprac.2021.104854

Download references

Funding

The research is supported by Key Laboratory of Construction Hydraulic Robots of Anhui Higer Education Institutes, Tongling University(Grant No.TLXYCHR-O-21ZD01). The research is supported by National Natural Science Foundation of China, Grant/Award Number: 52175083. The research is supported in part by The Pioneer Program Project of Zhejiang Province(Grant No. 2022C03018). The research is supported in part by The University Synergy Innovation Programof Anhui Province under Grant (Grant No.GXXT-2021-010). The research is supported in part by Key Research and Development Program of AnHui Province(Grant No. 2022a05020014).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Hefei University of Technology, Hefei, Anhui, 230009, PR China

    ShengChao Zhen & Yangyang Li

  2. School of Artificial Intelligence, Anhui University, Hefei, Anhui, 230601, PR China

    XiaoLi Liu

  3. Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui University, Hefei, Anhui, 230601, PR China

    XiaoLi Liu

  4. AnHui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Anhui University, Hefei, Anhui, 230601, PR China

    XiaoLi Liu

  5. The Geroge W.Woodrufi School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA

    Ye-Hwa Chen

Authors
  1. ShengChao Zhen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yangyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. XiaoLi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ye-Hwa Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yangyang Li, Shengchao Zhen and Xiaoli Liu. The first draft of the manuscript was written by Yangyang Li and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to XiaoLi Liu.

Ethics declarations

Conflict of Interest

The authors have no relevant fnancial or non-fnancial interests to disclose.

Ethics Approval

The research does not involve human participants, their data or biological material and it does not involve animals.

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

Zhen, S., Li, Y., Liu, X. et al. A Practical Model-Based Robust Control for the Modular Joint of Collaborative Robots. J Intell Robot Syst 108, 81 (2023). https://doi.org/10.1007/s10846-023-01914-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-023-01914-2

Keywords

Search

Navigation