Advanced Method for Motion Control of a 3 DOFs Lower Limb Rehabilitation Robot
Keywords:Rehabilitation robot, compensate gravity, motion control, inverse dynamic
This paper presents two motion control methods for a lower limb rehabilitation robot based on compensate gravity proportional-derivative and inverse dynamic proportional-derivative (PD) control algorithms. The Robot’s mechanism is comprised of three active joints: hip joint, knee joint and ankle joint, which are driven by DC motors. Firstly, based on Robot’s mechanism, a dynamic model of the Robot is built. Then, based on Robot’s model, motion control systems for Robot are designed. Simulation results show good performances and workability of these proposed controllers. Finally, the calculation of the joint angle errors and toque of each controller is performed. The comparison of simulation results between proposed controllers and the adaptive fuzzy controller allows to choice suitable motion control methods for Robot that can meet the requirements of a 3 DOFs lower limb rehabilitation robot for post-stroke patient.
 Wei Meng, Quan Liu, Zude Zhou, Qingsong Ai, Bo Seng, Sengquan Xie. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 2015, Vol. 31, p. 132-145.
 Jeremy L. Emken, Susan J. Harkema, Janell A. Beres-Jones, Christie K. Ferreira, and David J. Reinkensmeyer. Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Loco motor Training Following Spinal Cord Injury. IEEE Transactions on Biomedical Engineering, Vol. 5, No.1, p. 322-334.
 Vallery H, van Asseldonk EHF, Buss M, van der Kooij H. Reference trajectory generation for rehabilitation robots: complementary limb motion estimation. IEEE Transaction on Neural Systems Rehabilitation Engineering; Vol.17, No.1, p. 23–30.
 Duschau-Wicke A, von Zitzewitz J, Caprez A, Luenenburger L, Riener R. Path control: a method for patient-cooperative robot-aided gait rehabilitation. IEEE Transaction on Neural Systems Rehabilitation Engineering; Vol.18, p. 38–48.
 Saglia JA, Tsagarakis NG, Dai JS, Caldwell DG. Control strategies for patient assisted training using the ankle rehabilitation robot (ARBOT). IEEE/ASME Trans Mechatron 2012, p.1–10
 Renquan L, Zhijun L, Chun-Yi S, Anke X. Development and learning control of a human limb with a rehabilitation exoskeleton. IEEE Trans Ind Electron 2014; Vol. 61, p. 3776–85.
 Hussain S, Xie SQ, Jamwal PK. Control of a robotic orthosis for gait rehabilitation. Robot Autonom Syst 2013; Vol. 61(9), p. 911–19.
 Prashant K. Jamwal, Sheng Q. Xie, Shahid Hussain, and John G. Parsons. An Adaptive Wearable Parallel Robot for the Treatment of Ankle Injuries. IEEE/ASME Transactions on Mechatronics, 2014, Vol. 19(1), p. 64–75.
 Martin P, Emami MR. Aneuro-fuzzy approach to real-time trajectory generation for robotic rehabilitation. Robot Autonom Syst 2014; Vol. 62(4), p. 568–78.
 Nguyen Manh Tien. Industrial Robot Control. Science and Engineering Publishing House, 2007.
 Lorezo Sciavicco, Bruno Siciliano. Modling and control of Robot Manipulators. MacGraw Hill Companies Inc. 1996.
 Trung Hai Do, Duc Tan Vu. An Intelligent Control for Lower Limb Exoskeleton for Rehabilitation. SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – Volume 4 Issue 8 – August 2017, p. 13-19.