A Robust Linear Control Strategy to Enhance Damping of a Series Elastic Actuator on a Collaborative Robot

  • S. GhidiniEmail author
  • M. Beschi
  • N. Pedrocchi


Dealing with the physical interaction between humans and robots, Series Elastic Actuators (SEAs) are identified as one solution to overcome many limits, such as reducing contact forces or detect collisions. Nevertheless, the low-damping dynamic of a SEA can lead to undesired behaviours, especially during particular applications where a high level of precision is required. In this paper, a linear control architecture to enhance the damping performance of a SEA is presented. The proposed structure consists in a cascade control where loops are regulated using three types of controllers: PI, PD and a generalized controller specifically designed to damp oscillations. A frequency-domain approach with related constraints could not satisfy the time-domain goal in term of oscillation damping, for this reason an optimization problem able to consider them both is taken into account. A robust design is mandatory to the model mismatch introduced by neglecting coupling between motor. Therefore, robustness constraints are introduced in the optimization procedure. Indeed, the effectiveness of the control architecture is tested on a real compliant robot with six degrees of freedom equipped with as many SEAs. Each test aims to highlight the damping performance of the controlled system while the robot performs various tasks or it is subject to external disturbances.


Collaborative robots Tuning rules Series elastic actuator Damped control Robust control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



The work is partially supported by FourByThree Project H2020-FoF-06-2014-737095.

Compliance with Ethical Standards

Conflict of interests

None declared.


  1. 1.
    Sheridan, T.B.: Human–robot interaction: status and challenges. Hum. Factors 58(4), 525–532 (2016)CrossRefGoogle Scholar
  2. 2.
    Nemec, B., Likar, N., Gams, A., Ude, A.: Human robot cooperation with compliance adaptation along the motion trajectory. Auton. Robot. 42(5), 1023–1035 (2018)CrossRefGoogle Scholar
  3. 3.
    Alqaudi, B., Modares, H., Ranatunga, I., Tousif, S.M., Lewis, F.L., Popa, D.O.: Model reference adaptive impedance control for physical human–robot interaction. Control Theory and Technology 14(1), 68–82 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    De Santis, A., Siciliano, B., De Luca, A., Bicchi, A.: An atlas of physical human–robot interaction. Mech. Mach. Theory 43(3), 253–270 (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Tian, Y., Chen, Z., Jia, T., Wang, A., Li, L.: Sensorless collision detection and contact force estimation for collaborative robots based on torque observer. In: 2016 IEEE international conference on robotics and biomimetics (ROBIO), pp. 946–951. IEEE (2016)Google Scholar
  6. 6.
    Fritzsche, M., Elkmann, N., Schulenburg, E.: Tactile sensing: A key technology for safe physical human robot interaction. In: Proceedings of the 6th international conference on human–robot interaction (pp. 139–140). ACM (2011)Google Scholar
  7. 7.
    Magrini, E., Flacco, F., De Luca, A.: Control of generalized contact motion and force in physical human–robot interaction. In: 2015 IEEE international conference on robotics and automation (ICRA), pp. 2298–2304. IEEE (2015)Google Scholar
  8. 8.
    Dimeas, F., Avendano–Valencia, L.D., Aspragathos, N.: Human–robot collision detection and identification based on fuzzy and time series modelling. Robotica 33(9), 1886–1898 (2015)CrossRefGoogle Scholar
  9. 9.
    Lee, S.D., Song, J.B.: Sensorless collision detection based on friction model for a robot manipulator. Int. J. Precis. Eng. Manuf. 17(1), 11–17 (2016)CrossRefGoogle Scholar
  10. 10.
    Lo, S.Y., Cheng, C.A., Huang, H.P.: Virtual impedance control for safe human–robot interaction. J. Intell. Robot. Syst. 82(1), 3–19 (2016)CrossRefGoogle Scholar
  11. 11.
    Machairas, K., Papadopoulos, E.: An active compliance controller for quadruped trotting. In: 2016 24th mediterranean conference on control and automation (MED) (pp. 743–748). IEEE (2016)Google Scholar
  12. 12.
    Pouya, S., Khodabakhsh, M., Sprowitz, A., Ijspeert, A.: Spinal joint compliance and actuation in a simulated bounding quadruped robot. Auton. Robot. 41(2), 437–452 (2017)CrossRefGoogle Scholar
  13. 13.
    Knabe, C., Seminatore, J., Webb, J., Hopkins, M., Furukawa, T., Leonessa, A., Lattimer, B.: Design of a series elastic humanoid for the DARPA Robotics Challenge. In: 2015 IEEE–RAS 15th international conference on humanoid robots (Humanoids) (pp. 738–743). IEEE (2015)Google Scholar
  14. 14.
    Tatsch, C., Ahmadi, A., Bottega, F., Tani, J., da Silva Guerra, R.: Dimitri: an Open–Source Humanoid Robot with Compliant Joint. J. Intell. Robot. Syst. 91(2), 291–300 (2018)CrossRefGoogle Scholar
  15. 15.
    Pratt, G.A., Williamson, M.M.: Series elastic actuators. In Intelligent Robots and Systems 95.’Human Robot Interaction and Cooperative Robots’, Proceedings. 1995 IEEE/RSJ International Conference on (Vol. 1, pp. 399–406). IEEE (1995)Google Scholar
  16. 16.
    Paluska, D., Herr, H.: Series elasticity and actuator power output. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006. ICRA 2006. (pp. 1830–1833). IEEE (2006)Google Scholar
  17. 17.
    Paine, N., Mehling, J.S., Holley, J., Radford, N.A., Johnson, G., Fok, C.L., Sentis, L.: Actuator control for the NASA JSC Valkyrie humanoid robot: A decoupled dynamics approach for torque control of series elastic robots. J. Field Rob. 32(3), 378–396 (2015)CrossRefGoogle Scholar
  18. 18.
    Zhang, Q., Xu, B., Guo, Z., Xiao, X.: Design and modeling of a compact rotary series elastic actuator for an elbow rehabilitation robot. In: International conference on intelligent robotics and applications (pp. 44–56). Springer, Cham (2017)Google Scholar
  19. 19.
    Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z.: Human robot interaction control of rehabilitation robots with series elastic actuators. IEEE Trans. Robot. 31(5), 1089–1100 (2015)CrossRefGoogle Scholar
  20. 20.
    Park, H., Park, J., Lee, D.H., Park, J.H., Baeg, M.H., Bae, J.H.: Compliance–based robotic peg–in–hole assembly strategy without force feedback. IEEE Trans. Ind. Electron. 64(8), 6299–6309 (2017)CrossRefGoogle Scholar
  21. 21.
    Deimel, R., Brock, O.: A novel type of compliant and underactuated robotic hand for dexterous grasping. Int. J. Robot. Res. 35(1–3), 161–185 (2016)CrossRefGoogle Scholar
  22. 22.
    Ghidini, S., Beschi, M., Pedrocchi, N., Visioli, A.: Robust Tuning Rules for Series Elastic Actuator PID Cascade Controllers. IFAC–PapersOnLine 51(4), 220–225 (2018). Google Scholar
  23. 23.
    Simoni, L., Beschi, M., Legnani, G., Visioli, A.: Modelling the temperature in joint friction of industrial manipulators. Robotica, 1–22 (2018)Google Scholar
  24. 24.
    Johanastrom, K., Canudas-de-Wit, C.: Revisiting the LuGre friction model. IEEE Control. Syst. Mag. 28 (6), 101–114 (2008). 10.1109/MCS.2008.929425MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Wang, M., Sun, L., Yin, W., Dong, S., Liu, J.: Continuous robust control for series elastic actuator with unknown payload parameters and external disturbances. IEEE/CAA Journal of Automatica Sinica 4(4), 620–627 (2017)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Grun, M., Muller, R., Konigorski, U.: Model based control of series elastic actuators. In: 2012 4th Ieee Ras & Embs international conference on biomedical robotics and biomechatronics (Biorob) (pp. 538–543). IEEE (2012)Google Scholar
  27. 27.
    Vallery, H., Veneman, J., Van Asseldonk, E., Ekkelenkamp, R., Buss, M., Van Der Kooij, H.: Compliant actuation of rehabilitation robots. IEEE Robot. Autom. Mag. 15(3), 60–69 (2008)CrossRefGoogle Scholar
  28. 28.
    Oh, S., Kong, K.: High–precision robust force control of a series elastic actuator. IEEE/ASME Trans. Mechatron. 22(1), 71–80 (2017)CrossRefGoogle Scholar
  29. 29.
    Calanca, A., Fiorini, P.: Impedance control of series elastic actuators based on well–defined force dynamics. Robot. Auton. Syst. 96, 81–92 (2017)CrossRefGoogle Scholar
  30. 30.
    Calanca, A., Muradore, R., Fiorini, P.: Impedance control of series elastic actuators: Passivity and acceleration–based control. Mechatronics 47, 37–48 (2017)CrossRefGoogle Scholar
  31. 31.
    Veronesi, M., Visioli, A.: Simultaneous closed–loop automatic tuning method for cascade controllers. IET Control Theory Appl. 5(2), 263–270 (2011)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Qiu, J., Sun, K., Wang, T., Gao, H.: Observer-based fuzzy adaptive event-triggered control for pure-feedback nonlinear systems with prescribed performance. IEEE Transactions on Fuzzy Systems. (2019)
  33. 33.
    Roveda, L., Haghshenas, S., Prini, A., Dinon, T., Pedrocchi, N., Braghin, F., Tosatti, L.M.: Fuzzy impedance control for enhancing capabilities of humans in onerous tasks execution. In: 2018 15th international conference on ubiquitous robots (UR) (pp. 406-411). IEEE. (2018)
  34. 34.
    Sun, K., Mou, S., Qiu, J., Wang, T., Gao, H: Adaptive fuzzy control for non-triangular structural stochastic switched nonlinear systems with full state constraints. IEEE Transactions on Fuzzy Systems. (2018)
  35. 35.
    Roveda, L., Vicentini, F., Pedrocchi, N., Braghin, F., Tosatti, L.M.: Impedance shaping controller for robotic applications involving interacting compliant environments and compliant robot bases, Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, pp. 2066–2071. (2015)
  36. 36.
    Ghidini, S.: Damped Control for a SEA Collaborative Robot. Retrieved from (2019)

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Institute of Intelligent Industrial Technologies and Systems for Advanced ManufacturingNational Research Council of ItalyMilanItaly

Personalised recommendations