Page 66 - Read Online

Page 144 Tong et al. Intell Robot 2024;4:125-45 I http://dx.doi.org/10.20517/ir.2024.08

10. Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S. A survey on robotic devices for upper limb rehabilitation. J
Neuroeng Rehabil 2014;11:3. DOI
11. Klamroth-Marganska V, Blanco J, Campen K, et al. Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre,
parallel-group randomised trial. Lancet Neurol 2014;13:159-66. DOI
12. Nef T, Mihelj M, Colombo G, Riener R. ARMin - robot for rehabilitation of the upper extremities. In: Proceedings 2006 IEEE International
Conference on Robotics and Automation; 2006 May 15-19; Orlando, USA. IEEE; 2006. pp. 3152-7. DOI
13. Kim B, Deshpande AD. An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: design, modeling,
control, and performance evaluation. Int J Robot Res 2017;36:414-35. DOI
14. Islam MR, Assad-Uz-Zaman M, Brahmi B, Bouteraa Y, Wang I, Rahman MH. Design and development of an upper limb rehabilitative
robot with dual functionality. Micromachines 2021;12:870. DOI
15. Huang J, Tu X, He J. Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies.
IEEE T Syst Man Cybern Syst 2016;46:926-35. DOI
16. Lee HD, Lee BK, Kim WS, Han JS, Shin KS, Han CS. Human-robot cooperation control based on a dynamic model of an upper limb
exoskeleton for human power amplification. Mechatronics 2014;24:168-76. DOI
17. Ball SJ, Brown IE, Scott SH. MEDARM: a rehabilitation robot with 5DOF at the shoulder complex. In: 2007 IEEE/ASME International
Conference on Advanced Intelligent Mechatronics; 2007 Sep 04-07; Zurich, Switzerland. IEEE; 2007. p. 1-6. DOI
18. Brahmi B, Saad M, Luna CO, Archambault PS, Rahman MH. Passive and active rehabilitation control of human upper-limb exoskeleton
robot with dynamic uncertainties. Robotica 2018;36:1757-79. DOI
19. Ott C, Albu-Schaffer A, Kugi A, Stamigioli S, Hirzinger G. A passivity based cartesian impedance controller for flexible joint robots -
part I: torque feedback and gravity compensation. In: IEEE International Conference on Robotics and Automation; 2004 Apr 26 - May
01; New Orleans. IEEE; 2004. pp. 2659-65. DOI
20. Kim WS, Lee HD, Lim DH, Han JS, Shin KS, Han CS. Development of a muscle circumference sensor to estimate torque of the human
elbow joint. Sensor Actuat A Phys 2014;208:95-103. DOI
21. Radke A, Gao Z. A survey of state and disturbance observers for practitioners. In: 2006 American Control Conference; 2006 Jun 14-16;
Minneapolis, USA. IEEE; 2006. DOI
22. Rosen J, Brand M, Fuchs MB, Arcan M. A myosignal-based powered exoskeleton system. IEEE Trans Syst Man Cybern A 2001;31:210-2.
DOI
23. Ison M, Artemiadis P. The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control.
J Neural Eng 2014;11:051001. DOI
24. Hashemi J, Morin E, Mousavi P, Mountjoy K, Hashtrudi-Zaad K. EMG-force modeling using parallel cascade identification. J Elec-
tromyogr Kines 2012;22:469-77. DOI
25. Khan AM, Yun D, Zuhaib KM, et al. Estimation of desired motion intention and compliance control for upper limb assist exoskeleton.
Int J Control Autom Syst 2017;15:802-14. DOI
26. Yin W, Sun L, Wang M, Liu J. Nonlinear state feedback position control for flexible joint robot with energy shaping. Robot Auton Syst
2018;99:121-34. DOI
27. Hu J, Xiong R. Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter. IEEE T Ind
Electron 2018;65:3365-75. DOI
28. Martinez S, Garcia-Haro JM, Victores JG, Jardon A, Balaguer C. Experimental robot model adjustments based on force-torque sensor
information. Sensors 2018;18:836. DOI
29. Davari SA, Rodriguez J. Predictive direct voltage control of induction motor with mechanical model consideration for sensorless applica-
tions. IEEE J Em Sel Top P 2018;6:1990-2000. DOI
30. Chen S, Luo M, Jiang G, Abdelaziz O. Collaborative robot zero moment control for direct teaching based on self-measured gravity and
friction. Int J Adv Robot Syst 2018;15:1729881418808711. DOI
31. Jin X, Chen K, Zhao Y, Ji J, Jing P. Simulation of hydraulic transplanting robot control system based on fuzzy PID controller. Measurement
2020;164:108023. DOI
32. Huang Y, Yasunobu S. A general practical design method for fuzzy PID control from conventional PID control. In: Ninth IEEE Interna-
tional Conference on Fuzzy Systems; 2000 May 07-10; San Antonio, USA. IEEE; 2000. pp. 969-72. DOI
33. Aprile I, Cruciani A, Germanotta M, et al. Upper limb robotics in rehabilitation: an approach to select the devices, based on rehabilitation
aims, and their evaluation in a feasibility study. Appl Sci 2019;9:3920. DOI
34. Merat F. Introduction to robotics: mechanics and control. IEEE J Robot Autom 1987;3:166. DOI
35. Qin Z, Baron L, Birglen L. A new approach to the dynamic parameter identification of robotic manipulators. Robotica 2010;28:539-47.
DOI
36. Atkeson CG, An CH, Hollerbach JM. Estimation of inertial parameters of manipulator loads and links. Int J Robot Res 1986;5:101-19.
DOI
37. Swevers J, Verdonck W, De Schutter J. Dynamic model identification for industrial robots. IEEE Contr Syst Mag 2007;27:58-71. DOI
38. Jarrasse N, Proietti T, Crocher V, et al. Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients.
Front Hum Neurosci 2014;8:947. DOI
39. Aguirre-Ollinger G, Colgate JE, Peshkin MA, Goswami A. Inertia compensation control of a one-degree-of-freedom exoskeleton for
lower-limb assistance: initial experiments. IEEE Trans Neural Syst Rehab Eng 2012;20:68-77. DOI
40. Juang CF, Lai MG, Zeng WT. Evolutionary fuzzy control and navigation for two wheeled robots cooperatively carrying an object in