A 7-DoF Upper Limb Exoskeleton Robot Control Using a New Robust Hybrid Controller

  • Mehran RahmaniEmail author
  • Mohammad Habibur Rahman
  • Jawhar Ghommam
Regular Papers Robot and Applications


This article describes a novel robust hybrid control of a 7-DoF upper limb exoskeleton robot. Insensivity can be taken into consideration as the important feature of sliding mode controllers, which is merely valid in the sliding phase. In order to improve controller performance by eliminating or minimizing the time to reach the sliding phase, a sliding surface design can be designed appropriately. In this study, a new sliding mode controller is designed to achieve high performance, which is robust against external disturbances. PID controllers have been widely used in robotic systems since high trajectory can be one of the most important advantages of this controller. Therefore, by combining a new sliding mode controller and a conventional PID controller, a novel hybrid controller is proposed which has excellent performance. Simulation results demonstrate the effectiveness of the proposed control method.


Hybrid control method PID controller sliding mode controller upper limb exoskeleton robot 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    L. Guangye, W. Ye, and Q. Xie, “PID control for the robotic exoskeleton: application to lower extremity rehabilitation,” Proc. of IEEE Conference on Mechatronics and Automation (ICMA), pp. 2345–2350, Chengdu, China, 2012.Google Scholar
  2. [2]
    W. Qingcong, X. Wang, F. Du, and X. Zhang, “Design and control of a powered hip exoskeleton for walking assistance,” International Journal of Advanced Robotic Systems, vol. 12, no. 3, 2015.Google Scholar
  3. [3]
    Y. Wen, and J. Rosen, “Neural PID control of robot manipulators with application to an upper limb exoskeleton,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp.73-684, 2013.Google Scholar
  4. [4]
    Y. Wen, J. Rosen, and X. Li, “PID admittance control for an upper limb exoskeleton,” Proc. of IEEE Conference on American Control Conference (ACC), pp. 1124–1129, San Francisco, CA, USA, 2011.Google Scholar
  5. [5]
    Y. Wen, X. Li, and R. Carmona, “A novel PID tuning method for robot control,” Industrial Robot: An International Journal, vol. 40, no. 6, pp. 574–582, 2013.CrossRefGoogle Scholar
  6. [6]
    T. Yong, J. Zheng, Y. Lin, T. Wang, H. Xiong, G. He, and D. Xu, “Fuzzy PID control method of deburring industrial robots,” Journal of Intelligent & Fuzzy Systems, vol. 29, no. 6, pp. 2447–2455, 2015.CrossRefGoogle Scholar
  7. [7]
    R. Mehran, A. Ghanbari, and M. M. Ettefagh, “Robust adaptive control of a bio-inspired robot manipulator using bat algorithm,” Expert Systems with Applications, vol. 56, pp. 164–176, 2016.CrossRefGoogle Scholar
  8. [8]
    R. Mehran, H. Komijani, A. Ghanbari, and M. M. Ettefagh, “Optimal novel super-twisting PID sliding mode control of a MEMS gyroscope based on multi-objective bat algorithm,” Microsystem Technologies, vol. 24, no. 6, pp. 2835–2846, 2018.CrossRefGoogle Scholar
  9. [9]
    R. Mehran and A. Ghanbari, “Computed torque control of a caterpillar robot manipulator using neural network,” Advanced Engineering Forum, vol. 15, pp. 106–118, 2016.CrossRefGoogle Scholar
  10. [10]
    C. Dongkyoung, “Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates,” IEEE Trans. on Control Systems Technology, vol. 12, no. 4, pp. 637–644, 2004.CrossRefGoogle Scholar
  11. [11]
    Z. Man, A. P. Paplinski, and H. R. Wu, “A robust MIMO terminal sliding mode control scheme for rigid robotic manipulators,” IEEE Trans. on Automatic Control, vol. 39, no. 12, pp. 2464–2469, 1994.MathSciNetCrossRefzbMATHGoogle Scholar
  12. [12]
    Y. J. Min and J. H. Kim, “Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots,” IEEE Trans. on Robotics and Automation, vol. 15, no. 3, pp. 578–587,1999.CrossRefGoogle Scholar
  13. [13]
    B. Brahim, M. Saad, C. O. Luna, P. S. Archambault, and M. H. Rahman, “Sliding mode control of an exoskeleton robot based on time delay estimation,” Proc. of IEEE Conference on Virtual Rehabilitation (ICVR), pp. 1–2, Montreal, QC, Canada, 2017.Google Scholar
  14. [14]
    W. Xiaofeng, X. Li, J. Wang, X. Fang, and X. Zhu, “Datadriven model-free adaptive sliding mode control for the multi degree-of-freedom robotic exoskeleton,” Information Sciences, vol. 327, pp. 246–257, 2016.MathSciNetCrossRefzbMATHGoogle Scholar
  15. [15]
    R. M. Habibur, M. Saad, J. P. Kenné, and P. S. Archambault, “Nonlinear sliding mode control implementation of an upper limb exoskeleton robot to provide passive rehabilitation therapy,” Proc. of the Conf. on Intelligent Robotics and Applications, pp. 52–62. Springer, Berlin, Heidelberg, 2012.Google Scholar
  16. [16]
    B. Mahdieh, S. N. Goldar, M. H. Barhaghtalab, and V. Meigoli, “Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation,” Proc. of IEEE Conference on Robotics and Mechatronics, pp. 694–701, Tehran, Iran, 2015.Google Scholar
  17. [17]
    A. Ali, M. M. Arefi, F. Sedghi, and V. Abootalebi, “Robust Nonlinear Control Schemes for Finite-Time Tracking Objective of a 5-DOF Robotic Exoskeleton,” International Journal of Control, pp. 1–16, 2018.Google Scholar
  18. [18]
    C. Jinghui, S. Q. Xie, A. McDaid, and R. Das, “Sliding mode control of an exoskeleton gait rehabilitation robot driven by pneumatic muscle actuators,” Proc. of the 2015.Conf. on ASME International Design Engineering Technical Conferences and Computers and Information in Engineering, pp. V009T07A002-V009T07A002, Boston, Massachusetts, USA, 2015.Google Scholar
  19. [19]
    R. Amir and R. K. Moghaddam, “Fuzzy sliding mode control of 5 DOF upper-limb exoskeleton robot,” Proc. of IEEE Conference on Technology, Communication and Knowledge, pp. 25–32, Mashhad, Iran, 2015.Google Scholar
  20. [20]
    O. L. Cristóbal, M. H. Rahman, M. Saad, P. S. Archambault, and S. B. Ferrer, “Admittance-based upper limb robotic active and active-assistive movements,” International Journal of Advanced Robotic Systems, vol. 12, no. 9, 2015.Google Scholar
  21. [21]
    R. M. Habibur, C. O. Luna, Md. J. Rahman, M. Saad, and P. Archambault, “Force-position control of a robotic exoskeleton to provide upper extremity movement assistance,” International Journal of Modelling, Identification and Control, vol. 21, no. 4, pp. 390–400, 2014.CrossRefGoogle Scholar
  22. [22]
    R. M. Habibur, T. K. Ouimet, M. Saad, J. P. Kenné, and P. S. Archambault, “Development and control of a robotic exoskeleton for shoulder, elbow and forearm movement assistance,” Applied Bionics and Biomechanics, vol. 9, no. 3, pp. 275–292, 2012.CrossRefGoogle Scholar
  23. [23]
    L. C. Ochoa, M. H. Rahman, M. Saad, P. Archambault, and W. H. Zhu, “Virtual decomposition control of an exoskeleton robot arm,” Robotica, vol. 34, no. 7, pp. 1587–1609, 2016.CrossRefGoogle Scholar
  24. [24]
    J. J. Craig, Introduction to Robotics: Mechanics and Control, Pearson/Prentice Hall, Upper Saddle River, NJ, USA, 2005.Google Scholar
  25. [25]
    R. Mehran, A. Ghanbari, and M. M. Ettefagh, “Hybrid neural network fraction integral terminal sliding mode control of an Inchworm robot manipulator,” Mechanical Systems and Signal Processing, vol. 80, pp. 117–136, 2016.CrossRefGoogle Scholar
  26. [26]
    R. Mehran, A. Ghanbari, and M. M. Ettefagh, “A novel adaptive neural network integral sliding-mode control of a biped robot using bat algorithm,” Journal of Vibration and Control, vol. 24, no. 10, pp. 2045–2060, 2018.MathSciNetCrossRefGoogle Scholar
  27. [27]
    R. Mehran, “MEMS gyroscope control using a novel compound robust control,” ISA Transactions, vol. 72, pp. 37–43, 2018.CrossRefGoogle Scholar
  28. [28]
    K. K. Dad, W. Jie, and M. C. Lee, “Sensorless reaction force estimation of the end effector of a dual-arm robot manipulator using sliding mode control with a sliding perturbation observer,” International Journal of Control, Automation and Systems, vol. 16, no. 3, pp. 1367–1378, 2018.CrossRefGoogle Scholar
  29. [29]
    J. Seul, “Improvement of tracking control of a sliding mode controller for robot manipulators by a neural network,” International Journal of Control, Automation and Systems, vol. 16, no. 2, pp. 937–943, 2018.CrossRefGoogle Scholar

Copyright information

© ICROS, KIEE and Springer 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  2. 2.Department of Mechanical/Biomedical EngineeringUniversity of Wisconsin-MilwaukeeMilwaukeeUSA
  3. 3.Department of Electrical and Computer Engineering, College of EngineeringSultan Qaboos UniversityAl Khoudh, MuscutOman

Personalised recommendations