Optimizing the Torque of Knee Movements of a Rehabilitation Robot

Document Type: English


1 Msc student, Mechanica Engineering Department

2 Faculty member, Mechanical engineering department, Khomeinishahr Branch, Islamic Azad University

3 Islamic Azad University, Majlesi Branch


The aim of this study is to employ the novel Adaptive Network-based Fuzzy Inference System to optimize the torque applied on the knee link of a rehabilitation robot. Given the special conditions of stroke or spinal cord injury patients, devices with minimum error are required for performing the rehabilitation exercises. After examining the anthropometric data tables of human body, parameters such as the length of shins, weight, force, joint angle etc. were chosen as the input data with the torque as the output of the system. Errors at any stage of the treatment can harm the patient or disrupt their recovery. Therefore, after examining different numbers of various fuzzy inference system membership functions and their consequent error, cases with the lowest error were chosen to be the best possible conditions for the system. Overall it can be said that a robot using the adaptive network-based fuzzy inference system offers negligible error.


[1] V. Khoshdel, A. Akbarzadeh and H. Moeenfard " Variable Impedance Control for Rehabilitation Robot using Interval Type-2 Fuzzy Logic" International Journal of Robotics, Vol. 4, No.3, pp. 46-54, 2015.

­ [2] N. Hogan, "Impedance control: An approach to manipulation: Part I, Part II, Part III," ASME J. Dynam. Syst.Measurement, Control.Trans, vol. 107, pp. 1-24, 1985.

[3] J. seul, T. C. Hsia, and R. G. Bonits, "Force tracking impedance control of robot manipulators under unknown environment," IEEE Transactions On Control Systems Technology, vol. 12, no. 3, 2004.

[4] H. I. Krebs, An overview of rehabilitation robotic technologies. American spinal injury association symposium, 2006.

[5] R. Riener, L. Lunenburger, S. Jezernik, and M. Anderschitz, "Patient-cooperative strategies for robot-aided treadmill training: first experimental results," IEEE Trans Rehabil Eng, vol. 13, pp. 380-394, 2005.

[6] L. Chun-De , H. Yi-Ching , L. Li-Fong, C. Yen-Shuo, T. Jui-Chen ,C.  Chun-Lung, L. Tsan-Hon      " Continuous passive motion and its effects on knee flexion after total knee arthroplasty in patients with knee osteoarthritis" Knee Surgery, Sports Traumatology, Arthroscopy, Vol. 24, Issue8, pp. 2578–2586, 2016.

[7] S. W. O'Driscoll and N. J. Giori, "Continuous passive motion (CPM): theory and principles of clinical application," Journal of Rehabilitation Research and Development, vol. 37, no. 2, pp. 179-188, 2000.

[8] D. Bradley, C. Marquez, M. Hawley, and S. Brownsell, "NeXOS –the design, development, and evaluation of a rehabilitation system for the lower limbs," Mechatronics, vol. 19, pp. 247-257, 2009.

 [9] P. Metrailler, V. Blanchard, I. Perrin, and R. Brodard, "Improvement of rehabilitation possibilities with the Motion Maker," Proceeding of the IEEE BioRob2006 conference, pp. 359-364, 2006.

[10] S. Moughamir, J. Zaytoon, N. Manamanni, and L. Afilal, "A system approach for control development of lower limbs training machines," Control Eng Pract, vol. 10, pp. 287-299, 2002.

[11] M. Bouri, B. Le. Gall, and R. Clavel , "A new concept of parallel robot for rehabilitation and fitness: The Lambda," Proceedings of the IEEE International Conference on Robotics and Biomimetics, pp. 2503-2508, 2009.

[12] E. Akdogan and M. Arif Adli, "The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot," Mechatronics, vol. 21, pp. 509-522, 2011.

[13] L. Marchal and D. Reinkensmeyer, "Review of control strategies for robotic movement training after neurologic injury," Journal of NeuroEngineering and Rehabilitation, vol. 6, pp. 1-20, 2009.

[14] E. Akdogan, M. Arif Adli, and E. Tacgm, "Knee rehabilitation using an intelligent robotic system," J Intell Manuf, vol. 20, pp. 195-202, 2009.

[15] R. Alan, J. Parkinson, B. John, and D. Hedengren, Optimization Methods for Engineering Design. Brigham Young University , 2013.

[16] J. K. George and B. Yuan, Fuzzy sets and Fuzzy Logic, Theory and applications.: Prentice Hall PTR, 1995.

[17] L. A. Zadeh, "Fuzzy Sets," Information and Control, vol. 8, pp. 338-353, 1965.


[18] J. Robert and C. Simon, "Type-2 Fuzzy Logic: A Historical View," Computational Intelligence Magazine, IEEE, vol. 2, pp. 57-62, 2007.


[19] Mohammad bagher menhaj, The Basics of the Neural Networks, Amir Kabir University of Technology Publications, 2002.


[20] Hassan Asgharzadeh, Artificial Intelligence: Payam Noor University Publications.

[21] J. Shing and R. Jang, "ANFIS: Adaptive Network-Based Fuzzy Inference Systems," IEEE Trans. On Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.

[22] M. Zhou, S. Li, and X. Yang, "Global Stability of Two Linear Non-autonomous Takagi–Sugeno Fuzzy Systems," Ecosystem Assessment and Fuzzy Systems Management. Springer International Publishing, pp. 147-155, 2014.

[23] Centers for Disease Control, Anthropometry Procedures Manual: National Health and Nutrition Examination Survey (NHANES), January 2016

[24] "Anthropometric data," University Of RHODE Island, Department Of Electrical, Computer and Biomedical Engineering, 2014.