A Redundant Parallel Robotic Machining Tool: Design, Control and Real-Time Experiments

  • Hussein SaiedEmail author
  • Ahmed Chemori
  • Micael Michelin
  • Maher El-Rafei
  • Clovis Francis
  • Francois Pierrot
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 175)


In this chapter, we present a machining device, named ARROW robot, designed with the architecture of a redundant parallel manipulator capable of executing five degrees-of-freedom in a large workspace. Machine-tools based on parallel robot development are considered a key technology of machining industries due to their favourable features such as high rigidity, good precision, high payload-to-weight ratio and high swiftness. The mechanism of ARROW robot isolates its workspace from any type of inside singularities allowing it to be more flexible and dynamic. An improved PID with computed feedforward controller is implemented on ARROW robot to perform real-time experiments of a machining task. The control system deals with antagonistic internal forces caused by redundancy through a regularization method, and achieves a stability conservation in case of actuators saturation. The results are evaluated using the root mean square error criteria over all the tracking trajectory confirming the high accuracy and good performance of ARROW robot in machining operations.


Machine tool Redundant parallel robot Kinematics Dynamics Singularity analysis Motion planning Control PID Feedforward Anti-windup 



This work has been supported by the ARPE ARROW project.


  1. Bennehar, M., Chemori, A., & Pierrot, F. (2014a). A new extension of desired compensation adaptive control and its real-time application to redundantly actuated PKMs. In Intelligent Robots and Systems (IROS), Chicago, IL, USA.Google Scholar
  2. Bennehar, M., Chemori, A., & Pierrot, F. (2014b). A novel RISE-based adaptive feedforward controller for redundantly actuated parallel manipulators. In Intelligent Robots and Systems (IROS), Chicago, IL, USA.Google Scholar
  3. Bennehar, M., Chemori, A., Pierrot, F., & Creuze, V. (2015). Extended model-based feedforward compensation in L1 adaptive control for mechanical manipulators: Design and experiments. Frontiers in Robotics and AI, 2, 32.CrossRefGoogle Scholar
  4. Bruzzone, L., Molfino, R., & Razzoli, R. (2002). Modelling and design of a parallel robot for lasercutting applications. In International Conference on Modeling, Identification and Control (IASTED’02), Innsbruck, Austria (pp. 518–522).Google Scholar
  5. Codourey, A., Honegger, M., & Burdet, E. (1997). A body-oriented method for dynamic modeling and adaptive control of fully parallel robots. In 5th Symposium Robot Control (pp. 443–450).CrossRefGoogle Scholar
  6. Davim, J. P. (2008). Machining: Fundamentals and recent advances. London: Springer Science & Business Media.Google Scholar
  7. eFunda. (2018). Machining: An introduction. In: eFunda, processes, machining. Available via DIALOG.
  8. Grange, S., Conti, F., & Rouiller, P. (2001). Overview of the delta haptic device. Eurohaptics, 1, 5–7.Google Scholar
  9. Kerbrat, O., Mognol, P., & Hascoët, J. Y. (2011). A new DFM approach to combine machining and additive manufacturing. Computers in Industry, 62(7), 684–692.CrossRefGoogle Scholar
  10. Kucuk, S. (2012). Serial and parallel robot manipulators – Kinematics, dynamics, control and optimization. Croatia: Intech.CrossRefGoogle Scholar
  11. Kumar, S., & Negi, R. (2012). A new DFM approach to combine machining and additive manufacturing. In 2nd International Conference on Power, Control and Embedded Systems.Google Scholar
  12. Li, Y., & Xu, Q. (2007). Design and development of a medical parallel robot for cardiopulmonary resuscitation. IEEE/ASME Transaction on Mechatronics, 12(3), 265–273.MathSciNetCrossRefGoogle Scholar
  13. Liégeois, A., Fournier, A., & Aldon, M. J. (1980). Model reference control of high-velocity industrial robots. In Joint Automatic Control Conference, San Francisco.Google Scholar
  14. Merlet, J. P. (2006). Introduction (chapter 1). Structural synthesis and architectures (chapter 2). In Gladwell, G. (Ed.), Parallel robots (Solid mechanics and its applications, 2nd ed.). The Netherlands: Springer.Google Scholar
  15. Muller, A. (2009). Effects of geometric imperfections to the control of redundantly actuated parallel manipulators. In IEEE International Conference on Robotics and Automation (ICRA’09) (pp. 1782–1787).Google Scholar
  16. Muller, A., & Hufnagel, T. (2011) A projection method for the elimination of contradicting control forces in redundantly actuated PKM. In IEEE International Conference on Robotics and Automation (ICRA’11) (pp. 3218–3223).Google Scholar
  17. Natal, G. S., Chemori, A., & Pierrot, F. (2012). Dual-space adaptive control of redundantly actuated parallel manipulators for extremely fast operations with load changes. In IEEE International Conference on Robotics and Automation (ICRA), Saint Paul, MN, USA.Google Scholar
  18. Natal, G. S., Chemori, A., & Pierrot, F. (2015). Dual-space control of extremely fast parallel manipulators: Payload changes and the 100G experiment. IEEE Transaction on Control Systems Technology, 23(4), 1520–1535.CrossRefGoogle Scholar
  19. Reyes, F., & Kelly, R. (2001). Experimental evaluation of model-based controllers on a direct-drive robot arm. Mechatronics, 11, 267–282.CrossRefGoogle Scholar
  20. Santibanez, V., & Kelly, R. (2000). PD control with feedforward compensation for robot manipulators: Analysis and experimentation. Robotica, 19, 11–19. Cambridge University Press.Google Scholar
  21. Shayya, S. (2015). Towards rapid and precise parallel kinematic machines. Ph.D. thesis, Université Montpellier (Ex UM2).Google Scholar
  22. Shayya, S., Krut, S., & Company, O. (2014). Dimensional synthesis of 4 dofs (3t-1r) actuatedly redundant parallel manipulator based on dual criteria: Dynamics and precision. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’14) (pp. 1716–1723).Google Scholar
  23. Shoham, M., Burman, M., & Zehavi, E. (2003). Bone-mounted miniature robot for surgical procedures: Concept and clinical applications. IEEE Transaction on Robotics and Automation, 19(5), 893–901.CrossRefGoogle Scholar
  24. Stewart, D. (1965). A platform with six degrees of freedom. Proceedings of the Institution of Mechanical Engineers, 180, 371–386. ARCHIVE.Google Scholar
  25. Su, Y., Duan, B., & Zheng, C. (2004). Nonlinear pid control of a six-dof parallel manipulator. IEEE Proceedings-Control Theory and Applications, 151, 95–102.CrossRefGoogle Scholar
  26. Yang, H. (2012). Agile mobile manufacturing for large workpieces. Ph.D. thesis, Université Montpellier.Google Scholar
  27. Youssef, H. A., & El-Hofy, H. (2008). Machining technology: Machine tools and operations. Boca Raton: Taylor & Francis.CrossRefGoogle Scholar
  28. Zhang, Y. X., Cong, S., & Shang, W. W. (2007). Modeling, identification and control of a redundant planar 2-dof parallel manipulator. International Journal of Control, Automation and Systems, 5, 559–569.Google Scholar
  29. Ziegler, J., & Nichols, N. (1942). Optimum settings for automatic controllers. Transaction of the American Society of Mechanical Engineer, 64, 759–768.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hussein Saied
    • 1
    • 2
    Email author
  • Ahmed Chemori
    • 1
  • Micael Michelin
    • 3
  • Maher El-Rafei
    • 2
  • Clovis Francis
    • 2
  • Francois Pierrot
    • 1
  1. 1.LIRMM, University of MontpellierCNRS, MontpellierFrance
  2. 2.CRSI, Lebanese UniversityBeirutLebanon
  3. 3.Tecnalia FranceCentre Spatial UniversitaireMontpellierFrance

Personalised recommendations