ANFIS-PID Control FES-Supported Sit-to-Stand in Paraplegics: (Simulation Study)


Adaptive Neuro-fuzzy Inference System (ANFIS) controller was designed to control knee joint during sit to stand movement through electrical stimuli to quadriceps muscles. The developed ANFIS works as an inverse model to the system (functional electrical stimulation (FES)-induced quadriceps-lower leg system), while there is a proportional-integral-derivative (PID) controller in the feedback control. They were designated as ANFIS-PID controller. To evaluate the ANFIS-PID controller, two controllers were developed: open loop and feedback controllers. The results showed that ANFIS-PID controller not only succeeded in controlling knee joint motion during sit to stand movement, but also reduced the deviations between desired trajectory and actual knee movement to ±5°. Promising simulation results provide the potential for feasible clinical application in the future.

Share and Cite:

Hussain, R. , Massoud, R. and Al-Mawaldi, M. (2014) ANFIS-PID Control FES-Supported Sit-to-Stand in Paraplegics: (Simulation Study). Journal of Biomedical Science and Engineering, 7, 208-217. doi: 10.4236/jbise.2014.74024.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Aissaoui, R. and Dansereau, J. (1999) Biomechanical Analysis and Modelling of Sit to Stand Task: A Literature Review. IEEE SMC’99 Conference Proceedings, Tokyo, 12-15 October 1999, 141-146.
[2] Gross, M., Stevenson, P., Charette, S., Pyka, G. and Marcus, R. (1998) Effect of Muscle Strength and Movement Speed on the Biomechanics of Rising from a Chair in Healthy Elderly and Young Women. Gait and Posture, 8, 175-180.
[3] Bromley, I. (2006) Tetraplegia and Parapliegia: A Guide for Physiotherapists. Elsevier, Philadelphia.
[4] Kantrowitz, A. (1960) Electronic Physiologie Aids: A Report of the Maimonides Hospital. Brooklyn, New York.
[5] Lynch, C. and Popovic, M. (2008) Functional Electrical Stimulation: Closed-Loop Control of Induced Muscle Contractions. IEEE Control System Magazine, 28, 40-49.
[6] Kuzelicki, J., Kamnik, R., Bajd, T., Obreza, P. and Benko, H. (2002) Paraplegics Standing up Using Multichannel FES and Arm Support. Journal of Medical Engineering and Technology, 26, 106-110.
[7] Horch, K. and Dhillon, G. (2004) Neuroprosthetics: Theory and Practice. World Scientific Publishing Co.
[8] Zhang, K. and Zhu, D. (2004) Simulation Study of FES-Assisted Standing up with Neural Network Control. 26th Annual International Conference of the IEEE EMBS, San Francisco.
[9] Davoodi, R. and Andrews, B. (1998) Computer Simulation of FES Standing up in Paraplegia: A Self-Adaptive Fuzzy Controller with Reinforcement Learning. IEEE Transactions on Rehabilitation Engineering, 6, 151-161.
[10] Abu Bakar, N. and Abdullah, A. (2011) Dynamic Simulation of Sit to Stand Exercise for Paraplegia. IEEE International Conference on Control System, Computing and Engineering, Malaysia, 114-118.
[11] Wang, S.L. (2001) Motion Simulation with Working Model 2D and MSC. Visual Nastran 4D. Journal of Computer and Information Science and Engineering, 1, 193-196.
[12] Winter, D. (1990) Biomechanics and Motor Control of Human Movement. 2nd Edition, Wiley-Interscience, New York.
[13] Rustin, C. (2010) Physiological Modelling and Dynamic Simulation of Human Walking. Faculty Polytechnique de Mons, Mons University, Mons.
[14] Song, D., Cheng, E., Brown, I., Davoodi, R. and Loeb, G. (2008) Virtual Muscle 4.0.1: Muscle Model for Matlab User’s Manual. Alfred Mann Institute, California.
[15] Riener, R. and Fuhr, T. (1998) Patient-Driven Control of FES Supported Standing up: A Simulation Study. IEEE Transactions on Rehabilitation Engineering, 6, 113-124.
[16] Edrich, T., Riener, R. and Quintern, J. (2000) Analysis of Passive Elastic Joint Moments in Paraplegics. IEEE Transactions on Biomedical Engineering, 47, 1058-1064.
[17] Jang, J.-S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transaction on Systems, Man, and Cybernetics, 665-685.
[18] Al-Hmouz, A., Shen, J., Al-Hmouz, R. and Yan, J. (2012) Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile. IEEE Transaction on Learning Technologies, 5, 226-237.
[19] Haugen, F. (2010) Ziegler-Nichols’ Open-Loop Method. TechTeach, 1-7.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.