Adaptive Modeling and Control of an Upper-Limb Rehabilitation Robot Using RBF Neural Networks

  • Liang Peng
  • Chen Wang
  • Lincong Luo
  • Sheng Chen
  • Zeng-Guang HouEmail author
  • Weiqun Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)


Robot-assisted rehabilitation following neurological injury is most successful when subject participation is maximized in the training tasks. Developing control strategies that can provide subject-specific assistance is accordingly an active area of research. For robot-assisted rehabilitation training, it is challenging to adapt the robotic assistance to each patient’s impairment, and model-based control methods in previous studies are difficult to implement because of the computational complexity of human-robot interaction dynamics and changes of human active efforts during rehabilitation exercises. This study implements adaptive modeling and control for an two-DOF upper-limb rehabilitation robot by combining an RBF-based feedforward controller with a feedback impedance controller. Simulation and experiment results show that, the RBF neural network is able to adaptively establish the human-robot dynamics as well as estimating the human efforts, and the impedance controller guarantees compliant human-robot interaction and regulates the maximal tolerated tracking error. Besides, the proposed controller is defined in the robot workspace, thus is easy to be generalized to be used for multi-DOFs exoskeleton-type rehabilitation robots.


Rehabilitation robot Adaptive control RBF neural networks Assist-as-needed 


  1. 1.
    Riener, R., Nef, T., Colombo, G.: Robot-aided neurorehabilitation of the upper extremities. Med. Biol. Eng. Comput. 43(1), 2–10 (2005)CrossRefGoogle Scholar
  2. 2.
    Lo, A.C., Guarino, P.D., Richards, L.G., Haselkorn, J.K.: Robot-assisted therapy for long-term upper-limb impairment after stroke. N. Engl. J. Med. 362(19), 1772–1783 (2010)CrossRefGoogle Scholar
  3. 3.
    Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S.: A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11(1), 3 (2014)CrossRefGoogle Scholar
  4. 4.
    Marchal-Crespo, L., Reinkensmeyer, D.J.: Review of control strategies for robotic movement training after neurologic injury. J. Neuroeng. Rehabil. 6(1), 20 (2009)CrossRefGoogle Scholar
  5. 5.
    Hogan, N., Krebs, H.I., Rohrer, B., Palazzolo, J.J.: Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. J. Rehabil. Res. Dev. 43(5), 605 (2006)CrossRefGoogle Scholar
  6. 6.
    Prange, G.B., Jannink, M.J., Groothuis-Oudshoorn, C.G., Hermens, H.J., IJzerman, M.J.: Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J. Rehabil. Res. Dev. 43(2), 171 (2006)Google Scholar
  7. 7.
    Reinkensmeyer, D.J., Wolbrecht, E., Bobrow, J.: A computational model of human-robot load sharing during robot-assisted arm movement training after stroke. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4019–4023 (2007)Google Scholar
  8. 8.
    Lotte, F., et al.: A review of classification algorithms for eeg-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)CrossRefGoogle Scholar
  9. 9.
    Nazmi, N., Abdul Rahman, M.A., Yamamoto, S.-I., Ahmad, S.A., Zamzuri, H. Mazlan, S.A.: A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors 6(8), 1304 (2016)CrossRefGoogle Scholar
  10. 10.
    Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G.: EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PloS one 7(12), e52618 (2012)CrossRefGoogle Scholar
  11. 11.
    Wang, W., et al.: Toward patients motion intention recognition: dynamics modeling and identification of ilegan LLRR under motion constraints. IEEE Trans. Syst. Man Cybern. Syst. 46(7), 980–992 (2016)CrossRefGoogle Scholar
  12. 12.
    Peng, L., Hou, Z.G., Peng, L., Wang, W.: Design of CASIA-ARM: a novel rehabilitation robot for upper limbs. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5611–5616 (2015)Google Scholar
  13. 13.
    Peng, L., Hou, Z.G., Peng, L., Luo, L., Wang, W.: Robot assisted rehabilitation of the arm after stroke: prototype design and clinical evaluation. Sci. China Inf. Sci. 60(7), 073201 (2017)CrossRefGoogle Scholar
  14. 14.
    Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice hall, Englewood Cliffs (1991)zbMATHGoogle Scholar
  15. 15.
    Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Peng, L., Hou, Z.G., Wang, W.: Dynamic modeling and control of a parallel upper-limb rehabilitation robot. In: Proceedings of IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 532–537 (2015)Google Scholar
  17. 17.
    Hogan, N.: An organizing principle for a class of voluntary movements. J. Neurosci. 4(11), 2745–2754 (1984)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Liang Peng
    • 1
  • Chen Wang
    • 1
    • 2
  • Lincong Luo
    • 1
    • 2
  • Sheng Chen
    • 1
    • 2
  • Zeng-Guang Hou
    • 1
    • 2
    • 3
    Email author
  • Weiqun Wang
    • 1
  1. 1.State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.CAS Center for Excellence in Brain Science and Intelligence TechnologyBeijingChina

Personalised recommendations