Skip to main content
SpringerLink
Log in

A Survey on Modeling Mechanism and Control Strategy of Rehabilitation Robots: Recent Trends, Current Challenges, and Future Developments

  • Robot and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

Due to an increasingly serious aging society and a large number of disabled civilians, the number of residents who need rehabilitation therapy is increasing rapidly in the past few years, but the rehabilitation therapists/treatments are in short supply in reality. In recent years, with the in-depth research and technology development of rehabilitation robots, this contradiction is expected to be overcome. In this paper, the modeling mechanisms of rehabilitation robots are firstly introduced based on the pneumatic artificial muscle drive, motor drive, and cable drive, respectively. Then some typical methodologies are presented to deal with various types of control constraints, namely the nonlinearity, the parameter uncertainty, the fatigue, and the time-delay, etc. Moreover, the advantages and disadvantages of some existing control approaches and driving modes are discussed in depth, including some notable experimental results. The key challenges and shortcomings of rehabilitation robots are analyzed and summarized, and the future development of rehabilitation robots has been prospected finally.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. National Bureau of Statistics of China, “2020-China Statistics Yearbook,” China Statistics Press, http://www.stats.gov.cn/tjsj/ndsj/2020/indexch.htm.

  2. China Disabled Persons’ Federation, “2019 Statistical Communique on the Development of Programs for Persons with Disabilities,” China Statistics Press, http://2021old.cdpf.org.cn/sjzx/.

  3. C. Peng, S. Chen, C. Lai, C. Chen, C. Chen, J. Mizrahi, and Y. Handa, “Review: Clinical benefits of functional electrical stimulation cycling exercise for subjects with central neurological impairments,” Journal of Medical and Biological Engineering, vol. 31, no. 1, pp. 1–11, 2011.

    Article  Google Scholar 

  4. S. D. Prior and P. Warner, “A new development in low cost pneumatic actuators,” Proc. of Fifth International Conference on Advanced Robotics’ Robots in Unstructured Environments, vol. 2, pp. 1590–1593, 1991.

    Article  Google Scholar 

  5. G. Klute, J. Czerniecki, and B. Hannaford, “McKibben artificial muscles: Pneumatic actuators with biomechanical intelligence,” Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No.99TH8399), pp. 221–226, 1999.

  6. S. Qian, B. Zi, W. Shang, and Q. Xu, “A review on cable-driven parallel robots,” Chinese Journal of Mechanical Engineering, vol. 31, no. 66, August 2018.

  7. P. Peckham and J. Knutson, “Functional electrical stimulation for neuromuscular applications,” Annual Review of Biomedical Engineering, pp. 327–360, March 2005.

  8. M. Bélanger, R. Stein, G. Wheeler, T. Gordon, and B. Leduc, “Electrical stimulation: Can it increase muscle strength and reverse osteopenia in spinal cord injured individuals?” Archives of Physical Medicine and Rehabilitation, vol. 81, no. 8, pp. 1090–1098, August 2000.

    Article  Google Scholar 

  9. T. Mohr, J. Pødenphant, F. Sørensen, H. Galbo, G. Thamsborg, and M. Kjær, “Increased bone mineral density after prolonged electrically induced cycle training of paralyzed limbs in spinal cord injured man,” Calcified Tissue International, vol. 61, pp. 22–25, 1997.

    Article  Google Scholar 

  10. M. Bellman, R. Downey, A. Parikh, and W. Dixon, “Automatic control of cycling induced by functional electrical stimulation with electric motor assistance,” IEEE Transactions on Automation Science and Engineering, vol. 14, no. 2, pp. 1225–1234, April 2017.

    Article  Google Scholar 

  11. Z. Yang, Y. Chen, Z. Tang, and J. Wang, “Surface EMG based handgrip force predictions using gene expression programming,” Neurocomputing, vol. 207, pp. 568–579, September 2016.

    Article  Google Scholar 

  12. J. Slotine and W. Li, “Adaptive manipulator control: A case study,” IEEE Transactions on Automatic Control, vol. 33, no. 11, pp. 995–1003, November 1988.

    Article  MATH  Google Scholar 

  13. H. Brandtstadter, “Sliding mode control of electromechanical systems,” Engineering, 2009.

  14. K. Almaghout, B. Tarvirdizadeh, K. Alipour, and A. Hadi, “Design and control of a lower limb rehabilitation robot considering undesirable torques of the patient’s limb,” Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 234, no. 12, pp. 1457–1471, December 2020.

    Article  Google Scholar 

  15. W. Li, S. Guo, and S. Ren, “Summary of fuzzy control theory,” Henan Science and Technology, no. 11, pp. 12–15, April 2019. (in Chinese)

  16. C. Chou and B. Hannaford, “Measurement and modeling of McKibben pneumatic artificial muscles,” IEEE Transactions on Robotics and Automation, vol. 12, no. 1, pp. 90–102, February 1996.

    Article  Google Scholar 

  17. G. Klute and B. Hannaford, “Accounting for elastic energy storage in McKibben artificial muscle actuators,” Journal of Dynamic Systems, Measurement, and Control, vol. 122, no. 2, pp. 386–388, 2000.

    Article  Google Scholar 

  18. Y. Sugimoto, K. Naniwa, and K. Osuka, “Stability analysis of robot motions driven by McKibben pneumatic actuator,” 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3049–3054, October 2010.

  19. K. Wickramatunge and T. Leephakpreeda, “Study on mechanical behaviors of pneumatic artificial muscle,” International Journal of Engineering Science, vol. 48, no. 2, pp. 188–198, February 2010.

    Article  Google Scholar 

  20. T. Minh, T. Tjahjowidodo, H. Ramon, and H. Brussel, “A new approach to modeling hysteresis in a pneumatic artificial muscle using the Maxwell-slip model,” IEEE/ASME Transactions on Mechatronics, vol. 16, no. 1, pp. 177–186, February 2011.

    Article  Google Scholar 

  21. X. Zang, Y. Liu, S. Heng, Z. Lin, and J. Zhao, “Position control of a single pneumatic artificial muscle with hysteresis compensation based on modified Prandtl-Ishlinskii model,” Bio-medical Materials and Engineering, vol. 28, no. 2, pp. 131–140, 2017.

    Article  Google Scholar 

  22. D. Pietrala, “The characteristics of a pneumatic muscle,” Proc. of EPJ Web of Conferences, vol. 143, May 2017.

  23. J. Takosoglu, P. Laski, S. Blasiak, G. Bracha, and D. Pietrala, “Determining the static characteristics of pneumatic muscles,” Measurement and Control, vol. 49, no. 2, pp. 62–71, March 2016.

    Article  Google Scholar 

  24. D. Reynolds, D. Repperger, C. Phillips, and G. Bandry, “Modeling the dynamic characteristics of pneumatic muscle,” Annals of Biomedical Engineering, vol. 31, pp. 310–317, March 2003.

    Article  Google Scholar 

  25. G. Andrikopoulos, J. Arvanitakis, S. Manesis, and G. Nikolakopoulos, “A switched system modeling approach for a pneumatic muscle actuator,” Proc. of IEEE International Conference on Industrial Technology, pp. 833–839, June 2012.

  26. G. Andrikopoulos, G. Nikolakopoulos, and S. Manesis, “Pneumatic artificial muscles: A switching model predictive control approach,” Control Engineering Practice, vol. 21, no. 12, pp. 1653–1664, December 2013.

    Article  Google Scholar 

  27. B. Wang, T. Wang, Z. Guo, S. Gan, and Y. Jin, “Modeling and sliding mode control of quadruped robot driven by pneumatic muscles,” Robot, vol. 39, no. 5, pp. 620–626, September 2017. (in Chinese)

    Google Scholar 

  28. T. Itto and K. Kogiso, “Hybrid modeling of McKibben pneumatic artificial muscle systems,” 2011 IEEE International Conference on Industrial Technology, pp. 65–70, April 2011.

  29. Q. Wang, W. Wang, D. Hao, and C. Yun, “Hysteresis modeling and application of Mckibben pneumatic artificial muscles,” Journal of Mechanical Engineering, vol. 55, no. 21, pp. 73–80, November 2019. (in Chinese)

    Article  Google Scholar 

  30. K. Urabe and K. Kogiso, “Hybrid nonlinear model of McKibben pneumatic artificial muscle systems incorporating a pressure-dependent coulomb friction coefficient,” Proc. of IEEE Conference on Control Applications (CCA), pp. 1571–1578, November 2015.

  31. D. Ba, T. Dinh, and K. Ahn, “An integrated intelligent nonlinear control method for a pneumatic artificial muscle,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 4, pp. 1835–1845, August 2016.

    Article  Google Scholar 

  32. J. Zhong, D. He, C. Zhao, Y. Zhu, and Q. Zhang, “An rehabilitation robot driven by pneumatic artificial muscles,” Journal of Mechanics in Medicine and Biology, vol. 20, no. 9, September 2020.

  33. T. Minh, B. Kamers, H. Ramon, and H. Brussel, “Modeling and control of a pneumatic artificial muscle manipulator joint-part I: Modeling of a pneumatic artificial muscle manipulator joint with accounting for creep effect,” Mechatronics, vol. 22, no. 7, pp. 923–933, October 2012.

    Article  Google Scholar 

  34. L. Zhang, J. Li, Y. Cui, M. Dong, B. Fang, and P. Zhang, “Design and performance analysis of a parallel wrist rehabilitation robot (PWRR),” Robotics and Autonomous Systems, vol. 125, March 2020.

  35. L. Zhang, J. Li, M. Dong, B. Fang, Y. Cui, S. Zuo, and K. Zhang, “Design and workspace analysis of a parallel ankle rehabilitation robot (PARR),” Journal of Healthcare Engineering, vol. 2021, January 2021.

  36. J. Huang, X. Tu, J. He, and K. Zhang, “Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 46, no. 7, pp. 926–935, July 2016.

    Article  Google Scholar 

  37. ReWalk Robotics, “Argo Medical Technologies,” ISRAEL HEALTHCARE VENTURES, http://www.ihcv.co.il/portfolio-items/rewalk/.

  38. C. Phillips, J. Petrofsky, D. Hendershot, and D. Stafford, “Functional electrical exercise: A comprehensive approach for physical conditioning of the spinal cord injured patient,” Orthopedics, vol. 7, no. 7, pp. 1112–1123, July 1984.

    Article  Google Scholar 

  39. M. Alimanova, S. Borambayeva, D. Kozhamzharova, N. Kurmangaiyeva, D. Ospanova, G. Tyulepberdinova, G. Gaziz, and A. Kassenkhan, “Gamification of hand rehabilitation process using virtual reality tools: Using leap motion for hand rehabilitation,” Proc. of First IEEE International Conference on Robotic Computing (IRC), vol. 7, no. 7, pp. 336–339, May 2017.

    Google Scholar 

  40. L. Griffin, M. Decker, J. Hwang, B. Wang, K. Kitchen, Z. Ding, and J. Ivy, “Functional electrical stimulation cycling improves body composition, metabolic and neural factors in persons with spinal cord injury,” Journal of Electromyography and Kinesiology, vol. 19, no. 4, pp. 614–622, August 2009.

    Article  Google Scholar 

  41. C. Nelson, L. Nouaille, and G. Poisson, “A redundant rehabilitation robot with a variable stiffness mechanism,” Mechanism and Machine Theory, vol. 150, August 2020.

  42. Q. Yin, A. Hu, Q. Li, X. Wei, H. Yang, B. Wang, and G. Zhang, “Compound lower limb vibration training rehabilitation robot,” Concurrency and Computation: Practice and Experience, vol. 33, no. 6, October 2020.

  43. H. Yan, H. Wang, P. Chen, J. Niu, Y. Ning, S. Li, and X. Wang, “Configuration design of an upper limb rehabilitation robot with a generalized shoulder joint,” Applied Sciences, vol. 11, no. 5, pp. 1–20, February 2021.

    Google Scholar 

  44. M. Eslami, A. Mokhtarian, M. Pirmoradian, A. Seifzadeh, and M. Rafiaei, “Design and fabrication of a passive upper limb rehabilitation robot with adjustable automatic balance based on variable mass of end-effector,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 42, no. 629, November 2020.

  45. X. Xu, L. Hou, X. Huang, and W. Zhang, “Design and research of a wearable robot for upper limbs rehabilitation based on exoskeleton,” Robot (in Chinese), vol. 36, no. 2, pp. 147–155, March 2014.

    Google Scholar 

  46. F. Just, O. Ozen, S. Tortora, R. Riener, and G. Rauter, “Feedforward model based arm weight compensation with the rehabilitation robot ARMin,” International Journal of Control, pp. 72–77, July 2017.

  47. L. Zhang, S. Guo, and Q. Sun, “Development and assist-as-needed control of an end-effector upper limb rehabilitation robot,” Applied Sciences, vol. 10, no. 19, September 2020.

  48. Z. Sun, C. Wang, J. Wei, J. Xia, T. Wang, Q. Liu, L. Duan, Y. Wang, and J. Long, “Development of dual-arm lower limb rehabilitation robot for hemiplegic patients,” Proc. of the 38th Chinese Control Conference, pp. 1456–1461, July 2019.

  49. S. Kawasaki, K. Ohata, T. Tsuboyama, Y. Sawada, and Y. Higashi, “Development of new rehabilitation robot device that can be attached to the conventional knee-ankle-foot-orthosis for controlling the knee in individuals after stroke,” Proc. of International Conference on Rehabilitation Robotics (ICORR), pp. 304–307, August 2017.

  50. H. Wang, M. Lin, Y. Yan, G. Liu, B. Su, C. Zhao, and F. Wang, “Dynamics analysis and simulation of a new 6-DOF lower limb rehabilitation robot,” Proc. of the 2nd International Seminar on Computational Intelligence, Engineering and Technology (SCIET2018), vol. 490, no 6, pp. 408–413, November 2018.

    Google Scholar 

  51. T. Yang, X. Gao, and F. Dai, “New hybrid AD methodology for minimizing the total amount of information content: A case study of rehabilitation robot design,” Chinese Journal of Mechanical Engineering, vol. 33, no. 86, November 2020.

  52. J. Bae, S. Hwang, and I. Moon, “Evaluation and verification of a novel wrist rehabilitation robot employing safety-related mechanism,” Proc. of IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 288–293, July 2019.

  53. Y. Feng, H. Wang, L. Vladareanu, Z. Chen, and D. Jin, “New motion intention acquisition method of lower limb rehabilitation robot based on static torque sensors,” International Journal of Control, vol. 19, no. 15, August 2019.

  54. P. Tucan, C. Vaida, I. Ulinici, A. Banica, A. Burz, N. Pop, I. Birlescu, B. Gherman, N. Plitea, T. Antal, G. Carbone, and D. Pisla, “Optimization of the ASPIRE spherical parallel rehabilitation robot based on its clinical evaluation,” International Journal of Environmental Research and Public Health, vol. 18, no. 6, March 2021.

  55. M. Zhang, J. Cao, G. Zhu, Q. Miao, X. Zeng, and S. Xie, “Reconfigurable workspace and torque capacity of a compliant ankle rehabilitation robot (CARR),” Robotics and Autonomous Systems, vol. 98, pp. 213–221, December 2017.

    Article  Google Scholar 

  56. J. Wu, J. Gao, R. Song, R. Li, Y. Li, and L. Jiang, “The design and control of a 3 DOF lower limb rehabilitation robot,” Mechatronics, vol. 33, pp. 13–22, February 2016.

    Article  Google Scholar 

  57. C. Wang, L. Wang, T. Wang, H. Li, W. Du, F. Meng, and W. Zhang, “Research on an ankle joint auxiliary rehabilitation robot with a rigid-flexible hybrid drive based on a 2-S’PS’ mechanism,” Applied bionics and biomechanics, vol. 2019, July 2019.

  58. W. Zhang, S. Lu, L. Wu, X. Zhang, and H. Zhao, “Masterslave upper-limb exoskeleton rehabilitation robot training control method based on fuzzy compensation,” Robot (in Chinese), vol. 41, no. 1, pp. 123–145, January 2019.

    Google Scholar 

  59. H. Azcaray, A. Blanco, C. García, M. Adam, J. Reyes, G. Guerrero, and C. Guzmán, “Robust GPI control of a new parallel rehabilitation robot of lower extremities,” International Journal of Control, Automation, and Systems, vol. 16, no. 5, pp. 2383–2392, September 2018.

    Article  Google Scholar 

  60. C. Ma, N. Lu, and H. Jiang, “Study of FES cycling rehabilitation training system based on motor assistance,” China Academic Journal Electronic Publishing House (in Chinese), July 2010.

  61. A. Sunderland, D. Tinson, E. Bradley, D. Fletcher, R. Hewer, and D. Wade, “Enhanced physical therapy improves recovery of arm function after stroke. A randomised controlled trial,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 55, no. 7, pp. 530–535, July 1992.

    Article  Google Scholar 

  62. C. Rouse, C. Cousin, V. Duenas and W. Dixon, “Switched motorized assistance during switched functional electrical stimulation of the biceps brachii to compensate for fatigue,” Proc. of IEEE 56th Annual Conference on Decision and Control, pp. 5912–5918, December 2017.

  63. H. Jiang, C. Ma, N. Lu, and H. Ao, “Modeling and simulation on FES cycling training system,” Journal of System Simulation (in Chinese), vol. 22, no. 10, pp. 2459–2463, October 2010.

    Google Scholar 

  64. V. Duenas, C. Cousin, A. Parikh, P. Freeborn, E. Fox, and W. Dixon, “Motorized and functional electrical stimulation induced cycling via switched repetitive learning control,” IEEE Transactions on Control Systems Technology, vol. 27, no. 4, pp. 1468–1479, July 2019.

    Article  Google Scholar 

  65. C. Cousin, C. Rouse, and W. Dixon, “Split-crank functional electrical stimulation cycling: An adapting admitting rehabilitation robot,” IEEE Transactions on Control Systems Technology, vol. 29, no. 5, pp. 2153–2165, September 2021.

    Article  Google Scholar 

  66. C. Cousin, V. Duenas, and W. Dixon, “FES cycling and closed-loop feedback control for rehabilitative humanrobot interaction,” Robotics, vol. 10, no. 61, pp. 1–23, April 2021.

    Google Scholar 

  67. C. Rouse, C. Cousin, B. Allen, and W. Dixon, “Shared control for switched motorized FES-cycling on a split-crank cycle accounting for muscle control input saturation,” Automatica, vol. 123, January 2021.

  68. H. Yu, S. Huang, G. Chen, and N. Thakor, “Control design of a novel compliant actuator for rehabilitation robots,” Mechatronics, vol. 23, no. 8, pp. 1072–1083, December 2013.

    Article  Google Scholar 

  69. I. Hamida, M. Laribi, A. Mlika, L. Romdhane, S. Zeghloul, and G. Carbone, “Multi-objective optimal design of a cable driven parallel robot for rehabilitation tasks,” Mechanism and Machine Theory, vol. 156, February 2021.

  70. A. Mancisidor, A. Zubizarreta, I. Cabanes, P. Bengoa, and J. Jung, “Kinematical and dynamical modeling of a multipurpose upper limbs rehabilitation robot,” Robotics and Computer-integrated Manufacturing, vol. 49, pp. 374–387, February 2018.

    Article  Google Scholar 

  71. Y. Wang, K. Wang, Z. Zhang, L. Chen, and Z. Mo, “Mechanical characteristics analysis of a bionic muscle cable-driven lower limb rehabilitation robot,” Journal of Mechanics in Medicine and Biology, vol. 20, no. 10, December 2020.

  72. J. Niu, Q. Yang, G. Chen, and R. Song, “Nonlinear disturbance observer based sliding mode control of a cable-driven rehabilitation robot,” Proc. of International Conference on Rehabilitation Robotics (ICORR), pp. 664–669, August 2017.

  73. Y. Wang, K. Wang, W. Wang, P. Yin, and Z. Han, “Appraise and analysis of dynamical stability of cable-driven lower limb rehabilitation training robot,” Journal of Mechanical Science and Technology, vol. 33, no. 11, pp. 5461–5472, November 2019.

    Article  Google Scholar 

  74. E. Shata, K. Nguyen, P. Acharya, and J. Doom, “A series-elastic robot for back-pain rehabilitation,” International Journal of Control, Automation, and Systems, vol. 19, no. 2, pp. 1054–1064, October 2020.

    Article  Google Scholar 

  75. J. Perry, J. Rosen, and S. Burns, “Upper-limb powered exoskeleton design,” IEEE/ASME Transactions on Mechatronics, vol. 12, no. 4, pp. 408–417, August 2007.

    Article  Google Scholar 

  76. Y. Li, X. Yang, Y. Zhou, J. Chen, M. Du, and Y. Yang, “Adaptive stimulation profiles modulation for foot drop correction using functional electrical stimulation: A proof of concept study,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 1, pp. 59–68, January 2021.

    Article  Google Scholar 

  77. Y. Liu, H. Huang, T. Huang, Z. Kang, and J. Teng, “Controlling a rehabilitation robot with brain-machine interface: An approach based on independent component analysis and multiple kernel learning,” International Journal of Automation and Smart Technology, vol. 3, no. 1, pp. 67–75, 2013.

    Article  Google Scholar 

  78. R. Downey, M. Bellman, H. Kawai, C. Gregory, and W. Dixon, “Comparing the induced muscle fatigue between asynchronous and synchronous electrical stimulation in able-bodied and spinal cord injured populations,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 6, pp. 964–972, November 2015.

    Article  Google Scholar 

  79. C. Cousin, V. Duenas, C. Rouse, M. Bellman, P. Freeborn, E. Fox, and W. Dixon, “Closed-loop cadence and instantaneous power control on a motorized functional electrical stimulation cycle,” IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2276–2291, November 2020.

    Article  Google Scholar 

  80. R. Robinson, C. Kothera, R. Sanner, and N. Wereley, “Nonlinear control of robotic manipulators driven by pneumatic artificial muscles,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 1, pp. 55–68, February. 2016.

    Article  Google Scholar 

  81. N. Sun, D. Liang, Y. Wu, Y. Chen, Y. Qin, and Y. Fang, “Adaptive control for pneumatic artificial muscle systems with parametric uncertainties and unidirectional input constraints,” IEEE Transactions on Industrial Informatics, vol. 16, no. 2, pp. 969–979, February 2020.

    Article  Google Scholar 

  82. V. Ghanbari, V. Duenas, P. Antsaklis, and W. Dixon, “Passivity-based iterative learning control for cycling induced by functional electrical stimulation with electric motor assistance,” IEEE Transactions on Control Systems Technology, vol. 27, no. 5, pp. 2287–2294, September 2019.

    Article  Google Scholar 

  83. H. Quintero, R. Farris, K. Ha, and M. Goldfarb, “Preliminary assessment of the efficacy of supplementing knee extension capability in a lower limb exoskeleton with FES,” Proc. of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3360–3363, November 2012.

  84. Y. Zhao, C. Liang, Z. Gu, Y. Zheng, and Q. Wu, “A new design scheme for intelligent upper limb rehabilitation training robot,” International Journal of Environmental Research and Public Health, vol. 17, no. 8, April 2020.

  85. H. Huang, Y. Liu, W. Lin, Z. Kang, C. Cheng, and T. Huang, “Development of a P300 brain-machine interface and design of an elastic mechanism for a rehabilitation robot,” International Journal of Automation and Smart Technology, vol. 5, no. 2, pp. 123–145, June 2015.

    Google Scholar 

  86. Q. Wu, Q. Zhang, and C. Xiong, “Adaptive control of joint movement induced by electrical stimulation,” Acta Automatica Sinica (in Chinese), vol. 42, no. 12, pp. 1923–1932, December 2016.

    MATH  Google Scholar 

  87. X. Wang, X. Li, and J. Wang, “Active interaction exercise control of exoskeleton upper limb rehabilitation robot using model-free adaptive methods,” Acta Automatica Sinica (in Chinese), vol. 42, no. 12, pp. 1899–1914, December 2016.

    Google Scholar 

  88. G. Yin, X. Zhang, J. Chen, and W. Ma, “Trajectory tracking adaptive control of the lower limb rehabilitation robot with model uncertainty,” Journal of Electronic Measurement and Instrumentation (in Chinese), vol. 30, no. 11, pp. 1750–1757, November 2016.

    Google Scholar 

  89. F. Li, Z. Wu, and J. Qian, “Trajectory adaptation control for lower extremity rehabilitation robot,” Chinese Journal of Scientific Instrument (in Chinese), vol. 35, no. 9, pp. 2027–2036, September 2014.

    Google Scholar 

  90. M. Ayas and I. Altas, “Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot,” Control Engineering Practice, vol. 59, pp. 44–54, February 2017.

    Article  Google Scholar 

  91. J. Okazaki, A. Hoshina, and M. Sugaya, “Rehabilitation robot for elderly with estimation of stride,” Procedia Computer Science, vol. 112, pp. 2004–2013, 2017.

    Article  Google Scholar 

  92. J. Bai, A. Song, T. Wang, and H. Li, “A novel backstepping adaptive impedance control for an upper limb rehabilitation robot,” Computers & Electrical Engineering, vol. 80, December 2019.

  93. H. Asl and T. Narikiyo, “An assistive control strategy for rehabilitation robots using velocity field and force field,” Proc. of IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 790–795, July 2019.

  94. J. Wang, G. Zuo, C. Shi, and S. Guo, “A reward-punishment feedback control strategy based on energy information for wrist rehabilitation,” International Journal of Advanced Robotic Systems, vol. 17, no. 5, September 2020.

  95. S. Hasan and A. Dhingra, “An adaptive controller for human lower extremity exoskeleton robot,” Microsystem Technologies, vol. 27, pp. 2829–2846, January 2021.

    Article  Google Scholar 

  96. B. Brahmi, M. Driscoll, I. Bojairami, M. Saad, and A. Brahmi, “Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer,” ISA Transactions, vol. 108, pp. 381–392, February 2021.

    Article  Google Scholar 

  97. B. Zhang, J. Yao, X. Zhao, and X. Tan, “An adaptive human-robot interaction control method based on electromyography signals,” Control Theory & Applications, vol. 37, no. 12, pp. 2560–2570, December. 2020. (in Chinese)

    MATH  Google Scholar 

  98. Z. Shen, Y. Zhuang, J. Zhou, J. Gao, and R. Song, “Design and test of admittance control with inner adaptive robust position control for a lower limb rehabilitation robot,” International Journal of Control, Automation, and Systems, vol. 18, no. 1, pp. 134–142, August 2019.

    Article  Google Scholar 

  99. J. Yang, H. Su, Z. Li, D. Ao, and R. Song, “Adaptive control with a fuzzy tuner for cable-based rehabilitation robot,” International Journal of Control, Automation, and Systems, vol. 14, no. 3, pp. 865–875, June 2016.

    Article  Google Scholar 

  100. W. Qi, G. Zong, and W. Zheng, “Adaptive event-triggered SMC for stochastic switching systems with semi-Markov process and application to boost converter circuit model,” IEEE Transactions on Circuits and Systems I, vol. 68, no. 2, pp. 786–796, November 2020.

    Article  MathSciNet  Google Scholar 

  101. W. Qi, X. Gao, C. K. Ahn, J. Cao, and J. Cheng, “Fuzzy integral sliding-mode control for nonlinear semi-Markovian switching systems with application,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 3, pp. 1674–1683, 2022.

    Article  Google Scholar 

  102. K. Young, V. Utkin, and U. Ozguner, “A control engineer’s guide to sliding mode control,” IEEE Transactions on Control Systems Technology, vol. 7, no. 3, pp. 328–342, May 1999.

    Article  Google Scholar 

  103. K. Xing, J. Huang, Y. Wang, J. Wu, Q. Xu, and J. He, “Tracking control of pneumatic artificial muscle actuators based on sliding mode and non-linear disturbance observer,” IET Control Theory & Applications, vol. 4, no. 10, pp. 2058–2070, October 2010.

    Article  MathSciNet  Google Scholar 

  104. F. Estay, C. Rouse, M. Cohen, C. Cousin, W. Dixon, “Cadence and position tracking for decoupled legs during switched split-crank motorized FES-cycling,” Proc. of American Control Conference (ACC), pp. 854–859, July 2019.

  105. C. Cousin, C. Rouse, V. Duenas, and W. Dixon, “Controlling the cadence and admittance of a functional electrical stimulation cycle,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1181–1192, June 2019.

    Article  Google Scholar 

  106. V. Duenas, C. Cousin, V. Ghanbari, E. Fox, and W. Dixon, “Torque and cadence tracking in functional electrical stimulation induced cycling using passivity-based spatial repetitive learning control,” Automatica, vol. 115, May 2020.

  107. B. Brahmi, I. Bojairami, M. Saad, M. Driscoll, S. Zemam, and M. Laraki, “Enhancement of sliding mode control performance for perturbed and unperturbed nonlinear systems: Theory and experimentation on rehabilitation robot,” Journal of Electrical Engineering & Technology, vol. 16, pp. 599–616, December 2020.

    Article  Google Scholar 

  108. J. Niu, Q. Yang, X. Wang, and R. Song, “Sliding mode tracking control of a wire-driven upper-limb rehabilitation robot with nonlinear disturbance observer,” Frontiers in Neurology, vol. 8, no. 646, December 2017.

  109. S. Xie and P. Jamwal, “An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot,” Expert Systems with Applications, vol. 38, no. 7, pp. 8128–8137, July 2011.

    Article  Google Scholar 

  110. H. Li, D. Li, X. Chen, and Z. Pan, “Data-driven control based on the interval type-2 intuition fuzzy brain emotional learning network for the multiple degree-of-freedom rehabilitation robot,” Mathematical Problems in Engineering, vol. 2021, January 2021.

  111. A. Moshaii and M. Moghaddam, “Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger,” International Journal of Robotics Research and Application, vol. 46, no. 6, pp. 839–850, August 2019.

    Google Scholar 

  112. X. Li, Q. Yang, and R. Song, “Performance-based hybrid control of a cable-driven upper-limb rehabilitation robot,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 4, pp. 1351–1359, April 2021.

    Article  Google Scholar 

  113. K. Ahn and T. D. C. Thanh, “Improvement of the control performance of pneumatic artificial muscle manipulators using an intelligent switching control method,” KSME International Journal, vol. 18, pp. 1388–1400, August 2004.

    Article  Google Scholar 

  114. K. Ahn and H. Nguyen, “Intelligent switching control of a pneumatic muscle robot arm using learning vector quantization neural network,” Mechatronics, vol. 17, no. 4–5, pp. 255–262, May–June 2007.

    Article  Google Scholar 

  115. T. Thanh and K. Ahn, “Intelligent phase plane switching control of pneumatic artificial muscle manipulators with magneto-rheological brake,” Mechatronics, vol. 16, no. 2, pp. 85–95, March 2006.

    Article  Google Scholar 

  116. G. Andrikopoulos, G. Nikolakopoulos, I. Arvanitakis, and S. Manesis, “Switching model predictive control of a pneumatic artificial muscle,” International Journal of Control, Automation, and Systems, vol. 11, pp. 1223–1231, November 2013.

    Article  Google Scholar 

  117. G. Andrikopoulos, G. Nikolakopoulos, I. Arvanitakis, and S. Manesis, “Piecewise affine modeling and constrained optimal control for a pneumatic artificial muscle,” IEEE Transactions on Industrial Electronics, vol. 61, no. 2, pp. 904–916, March 2013.

    Article  Google Scholar 

  118. M. Bellman, T. Cheng, R. Downey, and W. Dixon, “Stationary cycling induced by switched functional electrical stimulation control,” Proc. of American Control Conference, pp. 4802–4809, July 2014.

  119. H. Kawai, M. Bellman, R. Downey, and W. Dixon, “Tracking control for FES-cycling based on force direction efficiency with antagonistic bi-articular muscles,” Proc. of American Control Conference, October 2013.

  120. M. Bellman, T. Cheng, R. Downey, C. Hass, and W. Dixon, “Switched control of cadence during stationary cycling induced by functional electrical stimulation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 12, pp. 1373–1383, December 2016.

    Article  Google Scholar 

  121. C. Rouse, C. Cousin, V. Duenas, and W. Dixon, “Cadence tracking for switched FES cycling combined with voluntary pedaling and motor resistance,” Proc. of Annual American Control Conference, pp. 4558–4563, August 2018.

  122. C. Rouse, C. Cousin, B. Allen, and W. Dixon, “Splitcrank cadence tracking for switched motorized FES-cycling with volitional pedaling,” Proc. of American Control Conference, pp. 4393–4398, July 2019.

  123. R. Downey, T. Cheng, M. Bellman, and W. Dixon, “Closed-loop asynchronous neuromuscular electrical stimulation prolongs functional movements in the lower body,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 6, pp. 1117–1127, November 2015.

    Article  Google Scholar 

  124. N. Kirsch, N. Alibeji, and N. Sharma, “Switching control of functional electrical stimulation and motor assist for muscle fatigue compensation,” Proc. of American American Control Conference, pp. 4865–4870, 2016.

  125. R. Downey, T. Cheng, M. Bellman, and W. Dixon, “Switched tracking control of the lower limb during asynchronous neuromuscular electrical stimulation: Theory and experiments,” IEEE Transactions on Cybernetics, vol. 47, no. 5, pp. 1251–1262, May 2017.

    Article  Google Scholar 

  126. C. Rouse, V. Duenas, C. Cousin, A. Parikh, and W. Dixon, “A switched systems approach based on changing muscle geometry of the biceps brachii during functional electrical stimulation,” IEEE Control Systems Letters, vol. 2, no. 1, pp. 73–78, July 2017.

    MathSciNet  Google Scholar 

  127. W. Nunes, R. Teodoro, M. Sanches, R. Araujo, M. Teixeira, and A. Carvalho, “Switched controller applied to functional electrical stimulation of lower limbs under fatigue conditions: A linear analysis,” XXVI Brazilian Congress on Biomedical Engineering, vol. 70, no. 1, pp. 383–390, June 2019.

    Article  Google Scholar 

  128. P. Artemiadis and K. Kyriakopoulos, “A switching regime model for the EMG-based control of a robot arm,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 1, pp. 53–63, February 2011.

    Article  Google Scholar 

  129. P. Pilarski, M. Dawson, T. Degris, J. Carey, and R. Sutton, “Dynamic switching and real-time machine learning for improved human control of assistive biomedical robots,” Proc. of the 4th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Roma, Italy, pp. 296–302, June 2012.

  130. J. Furukawa, T. Noda, T. Teramae and J. Morimoto, “Fault tolerant approach for biosignal-based robot control,” Advanced Robotics, vol. 29, no. 7, pp. 505–514, 2015.

    Article  Google Scholar 

  131. J. Xu, Y. Li, L. Xu, C. Peng, S. Chen, J. Liu, C. Xu, G. Cheng, H. Xu, Y. Liu, and J. Chen, “A multi-mode rehabilitation robot with magnetorheological actuators based on human motion intention estimation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 10, pp. 2216–2228, October. 2019.

    Article  Google Scholar 

  132. A. Denėve, S. Moughamir, L. Afilal, and J. Zaytoon, “Control system design of a 3-DOF upper limbs rehabilitation robot,” Computer Methods and Programs in Biomedicine, vol. 89, no. 2, pp. 202–214, February 2008.

    Article  Google Scholar 

  133. H. Yu, S. Huang, G. Chen, and N. Thakor, “Control design of a novel compliant actuator for rehabilitation robots,” Mechatronics, vol. 23, no. 8, pp. 1072–1083, December 2013.

    Article  Google Scholar 

  134. M. Mackowski, T. Grzejszczak, and A. Legowski, “An approach to control of human leg switched dynamics,” Proc. of 20th International Conference on Control Systems and Science, pp. 133–140, August 2015.

  135. A. Babiarz, A. Czornik, J. Klamka, M. Niezabitowski, and R. Zawiski, “The mathematical model of the human arm as a switched linear system,” Proc. of 19th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 508–513, November 2014.

  136. G. Chen, Z. Zhou, Y. Feng, R. Wang, N. Wang, and Q. Wang, “Improving the safety of ankle-foot rehabilitation system with hybrid control,” Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2015, pp. 700–705, July 2015.

  137. D. Erol and N. Sarkar, “Intelligent control framework for robotic rehabilitation after stroke,” Proc. of IEEE International Conference on Robotics and Automation, pp. 1238–1243, May 2007.

  138. J. Kim, J. Kim, H. Kim, and K. Park, “Development and evaluation of a hybrid walking rehabilitation robot, DDgo Pro,” International Journal of Precision Engineering and Manufacturing, vol. 21, pp. 2105–2115, September 2020.

    Article  Google Scholar 

  139. D. Wang, Y. Wang, B. Zi, Z. Cao, and H. Ding, “Development of an active and passive finger rehabilitation robot using pneumatic muscle and magnetorheological damper,” Mechanism and Machine Theory, vol. 147, pp. 2027–2036, May 2020.

    Google Scholar 

  140. J. Hua, L. He, Z. Kang, and K. Yan, “A force/position hybrid controller for rehabilitation robot,” International Journal of Computers Communications & Control, vol. 14, no. 5, pp. 615–628, October 2019.

    Article  Google Scholar 

  141. M. Shi, C. Yang, and D. Zhang, “A novel human-machine collaboration model of an ankle joint rehabilitation robot driven by EEG signals,” Mathematical Problems in Engineering, vol. 2021, March 2021.

  142. T. Shi, Y. Tian, Z. Sun, B. Zhang, Z. Pang, J. Yu, and X. Zhang, “A new projected active set conjugate gradient approach for Taylor-Type model predictive control: Application to lower limb rehabilitation robots with passive and active rehabilitation,” Frontiers in Neurorobotics, vol. 14, December 2020.

  143. S. Cai, Y. Chen, S. Huang, Y. Wu, H. Zheng, X. Li, and L. Xie, “SVM-based classification of sEMG signals for upper-limb self-rehabilitation training,” Frontiers in Neurorobotics, vol. 13, no. 31, June 2019.

  144. R. Kimura, T. Matsunaga, T. Iwami, D. Kudo, K. Saitoh, K. Hatakeyama, M. Watanabe, Y. Takahashi, N. Miyakoshi, and Y. Shimada, “Development of a rehabilitation robot combined with functional electrical stimulation controlled by non-disabled lower extremity in hemiplegic gait,” Progress in Rehabilitation Medicine, vol. 3, April 2018.

  145. X. Tu, H. Han, J. Huang, J. Li, C. Su, X. Jiang, and J. He, “Upper limb rehabilitation robot powered by PAMs cooperates with FES arrays to realize reach-to-grasp trainings,” Journal of Healthcare Engineering, vol. 2017, June 2017.

  146. X. Li, Z. Zhu, N. Shen, W. Dai, and Y. Hu, “Deeply feature learning by CMAC network for manipulating rehabilitation robots,” Future Generation Computer Systems, vol. 121, pp. 19–24, August 2021.

    Article  Google Scholar 

  147. C. Guzmán, A. Blanco, J. Brizuela, and F. Gómez, “Robust control of a hip-joint rehabilitation robot,” Biomedical Signal Processing and Control, vol. 35, pp. 100–109, May 2017.

    Article  Google Scholar 

  148. T. Goto, H. Dobashi, T. Yoshikawa, R. Loureiro, W. Harwin, Y. Miyamura, and K. Nagaie, “Utilization of kinematical redundancy of a rehabilitation robot to produce compliant motions under limitation on actuator performance,” IEEE International Conference on Rehabilitation Robotics, vol. 2017, pp. 646–651, May 1989.

    Google Scholar 

  149. Z. Zhou, B. Liang, G. Huang, B. Liu, J. Nong, and L. Xie, “Individualized gait generation for rehabilitation robots based on recurrent neural networks,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, no. 9, pp. 273–281, 2021.

    Article  Google Scholar 

  150. A. Passon, T. Schauer, and T. Seel, “Inertial-robotic motion tracking in end-effector-based rehabilitation robots,” Frontiers in Robotics and AI, vol. 7, November 2020.

  151. R. Cao, L. Cheng, C. Yang, and Z. Dong, “Iterative assist-as-needed control with interaction factor for rehabilitation robots,” Science China Technological Sciences, vol. 64, pp. 836–846, January 2021.

    Article  Google Scholar 

  152. R. Tao, R. Ocampo, J. Fong, A. Soleymani, and M. Tavakoli, “Modeling and emulating a physiotherapist’s role in robot-assisted rehabilitation,” International Journal of Control, vol. 2, July 2020.

  153. M. Joyo, Y. Raza, S. Ahmed, M. Billah, K. Kadir, K. Naidu, A. Ali, and Z. Yusof, “Optimized proportionalintegral-derivative controller for upper limb rehabilitation robot,” International Journal of Control, vol. 8, no. 8, July 2019.

  154. J. Catalán, A. Blanco, and N. Aracil, “Physiological reactions in single-player and competitive arm rehabilitation games,” Proc. of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 433–436, 2019.

  155. C. Cousin, C. Rouse, V. Duenas, and W. Dixon, “Position and torque control via rehabilitation robot and functional electrical stimulation,” Proc. of International Conference on Rehabilitation Robotics (ICORR), pp. 38–43, August 2017.

  156. X. Zhang, W. Li, J. Li, and X. Cai, “Research of the BWS system for lower extremity rehabilitation robot,” Proc. of International Conference on Rehabilitation Robotics (ICORR), pp. 240–245, August 2017.

  157. G. Xu, W. Chen, X. Gao, and A. Song, “Robot-aided resistance training method based on impedance identification and hybrid control,” Journal of Mechanical Engineering (in Chinese), vol. 52, no. 15, pp. 8–14, August 2016.

    Article  Google Scholar 

  158. F. Qin, H. Zhao, S. Zhen, H. Sun, and Y. Zhang, “Lyapunov based robust control for tracking control of lower limb rehabilitation robot with uncertainty,” International Journal of Control, Automation, and Systems, vol. 18, no. 1, pp. 76–84, November 2019.

    Article  Google Scholar 

  159. X. Zhu and J. Wang, “Double iterative compensation learning control for active training of upper limb rehabilitation robot,” International Journal of Control, Automation, and Systems, vol. 16, no. 3, pp. 1312–1322, June 2018.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mechanical Engineering and Automation, Huaqiao University, Xiamen, Fujian, 361021, China

    Yanzheng Zhu, Xianfang Tong & Chun-Yi Su

  2. School of Control Science and Engineering Shandong University, Jinan, Shandong, 250061, China

    Rongni Yang

  3. Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, 350002, China

    Yurong Li & Min Du

Authors
  1. Yanzheng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianfang Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rongni Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yurong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Min Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chun-Yi Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanzheng Zhu.

Additional information

on leave from the Department of Mechanical, Industrial, and Aerospace Engineering, Concordia University, QC, H3G 1M8, Canada

This work was supported in part by the National Natural Science Foundation of China under Grant no. 61973131, Grant no. 61873147, Grant no. 61773124, Grant no. 61733006, in part by the Fujian Outstanding Youth Science Fund under Grant no. 2020J06022, in part by the Scientific Research Funds of Huaqiao University under Grant no. 605-50Y19010, and in part by the Qilu Youth Scholar Programme from Shandong University, China, and the Youth Innovation Group Project of Shandong University, China (2020QNQT016).

Yanzheng Zhu received his Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China, in 2016. From 2013 to 2015, he was a joint Ph.D. student with the Department of Electrical and Computer Engineering, Ohio State University, Columbus, OH, USA. From 2016 to 2018, he was a Postdoctoral Researcher with the College of Electrical Engineering and Automation, Shandong University of Science and Technology. From 2017 to 2019, he was a Research Fellow with the School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, NSW, Australia. Since June 2019, he has been with Huaqiao University, Xiamen, China, where he is currently a full professor. His research interests include nondeterministic switched systems, network-based control systems, fault diagnosis and tolerant control, and their applications. He serves as an Associate Editor (Editorial Board Member) of Scientific Reports, and IEEE Access.

Xianfang Tong received his Bachelor’s degree in mechatronic engineering from Changchun University of Science and Technology, Changchun, China, in 2016. He is currently pursuing a Master’s degree in mechanical engineering from Huaqiao University, Xiamen, China. His current research interests include switched systems, rehabilitation robots, and their applications.

Rongni Yang received her B.S. degree in mathematics from Shandong University, China in 2006; an M.E. degree and a Ph.D. degree in control theory and control engineering both from Harbin Institute of Technology, China in 2008 and 2012, respectively. From 2009 to 2011, she was a Research Associate in the Faculty of Advanced Technology, University of South Wales, Pontypridd, U.K. From February 2013 to April 2013, she was a Research Associate in the Department of Mechanical Engineering, The University of Hong Kong. From February 2017 to February 2019, she was a Research Fellow in the School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, Australia. She is currently a Professor in the School of Control Science and Engineering at Shandong University, China. She currently serves as an Associate Editor for a number of international journals, including Circuits, Systems and Signal Processing, Electronics Letters, and IEEE Access. Her current research interests include networked control systems and multidimensional systems.

Yurong Li received her Master’s degree in industry automation and Ph.D. degree in control theory and control engineering from Zhejiang University, in 1997 and 2001, respectively. Now she is a professor at Fuzhou University. Since 2007, she is the member of Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology. Her research interests include biomedical instrument and intelligent information processing.

Min Du received her Ph.D. degree in electrical engineering from Fuzhou University in 2005. Now she is a professor and doctorial supervisor at Fuzhou University. Since 2007, she is the associate director of Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology. Her research interests include smart instrument and photoelectrical system.

Chun-Yi Su received his Ph.D. degree in control engineering from the South China University of Technology, Guangzhou, China, in 1990. In 1998, he joined Concordia University, Montreal, QC, Canada, after a seven-year stint with the University of Victoria, Victoria, BC, Canada. He is currently with the College of Mechanical Engineering and Automation, Huaqiao University, on leave from Concordia University. He has also held several short-time visiting positions, including the Chang Jiang Chair Professorship by the Chinas Ministry of Education and a JSPS Invitation Fellowship from Japan. He has authored or coauthored over 500 publications, which have appeared in journals, as book chapters, and in conference proceedings. His research covers control theory and its applications to various mechanical systems, with a recent focus on control of systems involving hysteresis nonlinearities. He has served as an Associate Editor for the IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, and IEEE Transactions on Cybernetics. He has been on the editorial board of several journals including IFACs Mechatronics. He is a Distinguished Lecturer of IEEE RA Society. He has served for many conferences as an Organizing Committee Member, including the General Chairs and the Program Chairs.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, Y., Tong, X., Yang, R. et al. A Survey on Modeling Mechanism and Control Strategy of Rehabilitation Robots: Recent Trends, Current Challenges, and Future Developments. Int. J. Control Autom. Syst. 20, 2724–2748 (2022). https://doi.org/10.1007/s12555-021-0571-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-0571-5

Keywords

Search

Navigation