Intelligent and classic Control of Rehabilitation Robot with Robust PID and Fuzzy Methods
Abstract
Rehabilitation of patients with neurological and spinal cord injuries is conducted for improving brain flexibility, as well as the patient performance. The performance of rehabilitation robots requires high standards of safety and reliability due to their direct interaction with humans during therapeutic motions. One of the simple robot arms is a two-degrees-of-freedom robot manipulator that its function is considered as the basis for the performance of other arms. Due to the nonlinear dynamics, the control of two-degree-of-freedom arm is challenging. At first, a robust PID controller was used, due to its simplicity and low-cost. Subsequently, the Mamdani and Sugeno fuzzy controllers were designed, in which two inputs, called error and error derivative, and torque output were included. Finally, the hybrid fuzzy controller was designed as the first joint of the robot by using robust PID control, and the second joint by using the Mamdani fuzzy control. Comparison of control methods showed that the fuzzy Momdani fuzzy controller at both axes can provide the most limited torque for the joint engines. In addition, the most accuracy of working point was the fuzzy hybrid control. Sugeno control showed the maximum speed and torque.
References
[2] E. Colgate, N.Hogan, “The Interaction of Robots with Passive Environments: Application to Force Feedback Control”, Advanced Robotics1989, pp. 465-474 Springer, 1989.
[3] E. Colgate, N.Hogan, “Robust control of dynamically interacting systems”, International Journal of Control, Vol. 48, No.1, pp. 65–88, 1988.
[4] H. Vallery, J. Veneman, E.Asseldonk, R. Ekkelenkamp, M. Buss, H. Kooij, “Compliant actuation of rehabilitation robots”, IEEE Robotics & Automation Magazine, Vol. 15 , Issue. 3 , pp. 60-69, 2008.
[5] K. Kong, J. Bae, M.Tomizuka, “Control of rotary series elastic actuator for ideal force-mode actuation in human robot interaction applications “, IEEE/ASME Transactions on mechatronics, Vol. 14, Issue. 1, pp. 105-118, 2009.
[6] S. Oh, K. Kong, “High-precision robust force control of a series elastic actuator”, IEEE/ASME Transactions on mechatronics, Vol. 22, Issue. 1, pp. 71–80, 2017.
[7] X. Li, Y.Pan, G. Chen, H.Yu, “Adaptive human-robot interaction control for robots driven by series elastic actuators”, IEEE Transactions on Robotics, Vol. 33, Issue. 1, pp. 169–182, 2017.
[8] H. Yu, S. Huang, G. Chen, Y. Pan, Z. Guo, “ Human robot interaction control of rehabilitation robots with series elastic actuators”, IEEE Transactions on Robotics, Vol. 31, Issue. 5, pp. 1089-1100, 2015.
[9] Y. Pan, H. Wang, X. Li, H. Yu, “Adaptive command-filtered back stepping control of robot arms with compliant actuators”. IEEE Transactions on Control systems Technology, Vol. 26, Issue. 3, pp. 1149-1156, 2018.
[10] H. I. Krebs, M. Ferraro, S. P. Buerger, M.J. Newbery, A. Makiyama, M. Sandmann, “Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus”. Journal of Neuro Engineering and Rehabilitation, Vol. 1, No. 5, pp. 1- 15, 2004.
[11] L. E. Kahn, P.S. Lum, W. Z. Rymer, D. J. Reinkensmeyer, “Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does? “, Journal of Rehabilitation Research & Development, Vol. 43, Issue. 5, pp. 619-630, 2006.
[12] J. J. Craig,” Introduction to Robotics: Mechanics and Control”, Pearson Education, Inc., 2005.
[13] R. J. Schilling, “Fundamentals of Robotics: Analysis and Control”, Simon & Schuster Trade, 1996.
[14] M.W. Spong, S. Hutchinson, M. Vidyasagar. “Robot modeling and control”, Wiley New Jersey, 2006.
[15] W. M. Silver “On the equivalence of Lagrangian and Newton-Euler dynamics for manipulators”, The International Journal of Robotics Research, Vol. 1, No. 2, pp. 60-70, 1982.
[16] A. Nawrocka, M. Nawrocki, A. Kot, “Fuzzy Logic Controller for Rehabilitation Robot Manipulator”, 15th International Carpathian Control Conference, Czech Republic, 2014.
[17] Y. Bai, D. Wang, “Fundamentals of Fuzzy Logic Control – Fuzzy Sets, Fuzzy Rules and Defuzzifications”, pp. 17-36, Springer, 2006.
[18] O. H. Adigun, “Decentralized Fuzzy-PID Based Control Model for a Multivariable Liquid Level System”, Journal of Advances in Computer Engineering and Technology, Vol.4, Issue.4, pp.247-254, 2018.
[19] S. Çetin , A.V. Akkaya, “Simulation and hybrid fuzzy-PID control for positioning of a hydraulic system”, Nonlinear Dynamics, Vol. 61, Issue. 3, pp. 465–476, 2010.
