Experimental Investigation for Two Links Wearable Rehabilitation Robot Dynamic According to Real Body Movement and Generate the Stability of the Trajectory Tracking by Classical PID Controller
DOI:
https://doi.org/10.12970/2308-8354.2020.08.01Keywords:
Herb induced liver injury, RUCAM, acute liver failure, multiorgan failure, hemorrhagic shock, shock liver, sepsis, kava, acetaminophen, amitriptyline, cannabis.Abstract
Stable trajectory tracking is one of the most important challenges in wearable rehabilitation robots design. Feedback controllers play a critical role to achieve this goal. There are different options to implement such a controller, e.g. PID, Fuzzy and hybrid systems. Traditionally, the performance of controllers is examined by try and error on hardware setups. It can be easily translated to more wasted time and cost. In this paper, we tried to make the design process more dynamic and efficient in. first, we proposed a simulation tool (based on NEXUSES® and MATLAB®) for a wearable rehabilitation robot with two links and 3 degrees-of-freedom, which duplicates elbow, wrist, and shoulder movement in 2-D space. Then, by the help of the tool we studied the benefits and functionality of one type of controller: classical PID. Our simulation result is verified that the Classical PID is improved the stability of trajectory tracking. Keywords: Classical PID, Wearable rehabilitation robot system, two links robots.References
[1] Tageldeen MK, Elamvazuthi I, Perumal N. (2016, April) Motion control for a multiple input rehabilitation wearableexoskeleton using fuzzy logic and PID. In 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC) (pp. 473-478). IEEE. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp. 68-73. https://doi.org/10.1109/AMC.2016.7496395
[2] Zhang H, Austin H, Buchanan S, Herman R, Koeneman J, He J. Feasibility studies of robot-assisted stroke rehabilitation at clinic and home settings using RUPERT. In 2011 IEEE International Conference on Rehabilitation Robotics IEEE 2011; pp. 16. https://doi.org/10.1109/ICCME.2011.5876812
[3] Rahman MH, Archambault PS, Saad M, Luna CO, Ferrer SB. Robot aided passive rehabilitation using 9th Asian. IEEE 2013; pp. 1-6.
[4] Rahman MH, Kittel-Ouimet T, Saad M, Kenné JP, Archambault PS. Dynamic Modeling and evaluation of a robotic exoskeleton for upper-limb rehabilitation. International Journal of Information Acquisition 2011; 8(01): 83-102. https://doi.org/10.1142/S0219878911002367
[5] Rahman MH, Ouimet TK, Saad M, Kenné JP, Archambault PS. Development of a 4DoFs exoskeleton robot for passive arm movement assistance. International Journal of Mechatronics and Automation 2012; 2(1): 3450. https://doi.org/10.1504/IJMA.2012.046587
[6] Zhu X, Wang J, Wang X. Nonlinear iterative learning control of 5 DOF upper-limb rehabilitation robot. In 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE 2015; pp. 793-798. https://doi.org/10.1109/ROBIO.2015.7418866