FUZZY LOGIC CONTROL FOR ANKLE FOOT ORTHOSES EQUIPPED WITH MAGNETORHEOLOGICAL BRAKE
DOI:
https://doi.org/10.11113/.v78.7942Keywords:
Ankle foot orthosis, foot drop, passive control, walking gait, electromyography bio signal, fuzzy logic controller, walking experimentAbstract
This study focused on the development of passive control ankle foot orthosis (PICAFO) for a specific purpose such as preventing foot drop in the post-stroke patient. The PICAFO utilized the magnetorheological (MR) brake as the actuator in which the braking torque was controlled by regulating direct current (DC) from current driver. The Fuzzy Logic Controller (FLC) was employed to control output voltage for current driver based on the inputs, i.e. Electromyography (EMG) bio signal and ankle position. Walking experiment to test the controller was carried out on a single subject where the input and output FLC was monitored and logged. The results showed that the output voltage of the FLC was 94.41% of the maximum output (high) on forward ankle position during swing phase and gradually increase from 9.667% to 77.34% of maximum output during stance phase. The FLC successfully controlled the output voltage according to the required needs. According to the experimental results, the FLC strategy was applicable for PICAFO realizing it contributes to prevention of foot drop.
References
Kikuchi, T., Tanaka, T., Shoji, A., Tanida, S., and Kato, M. 2011. Gait Measurement System To Develop Control Model Of Intelligently Controllable Ankle-Foot Orthosis. IEEE/SICE International Symposium on System Integration. 124-129.
Jiménez-Fabián, R., and Verlinden, O. 2012. Review Of Control Algorithms For Robotic Ankle Systems In Lower Limb Orthoses, Prostheses, And Exoskeletons. Medical Engineering and Physics. 34(4): 397-408.
Holgate, M. A., Böhler, A. W., and Sugar, T. G. 2008. Control Algorithms For Ankle Robots: A Reflection On The State-Of-The-Art And Presentation Of Two Novel Algorithms. IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. 97-102.
Böehler, A. W., Hollander, K. W., Sugar, T. G., and Shin, D. 2008. Design, Implementation And Test Results Of A Robust Control Method For A Powered Ankle Foot Orthosis AFO. IEEE International Conference on Robotics and Automation. 2025-30.
Gordon, K. E., Gregory, S. S., and Daniel, P. F. 2006. Mechanical Performance Of Artificial Pneumatic Muscles To Power An Ankle–Foot Orthosis. Neural Networks. 39(10): 1832-41.
Fleischer, C., and Hommel, G. 2006. EMG-driven Human Model For Orthosis Control. Human Interaction with Machines. 69-76.
Ma, L., Yang, Y., Chen, N., Song, R., and Li, L. 2015. Effect Of Different Terrains On Onset Timing, Duration And Amplitude Of Tibialis Anterior Activation. Biomedical Signal Processing and Control. 19: 115-121.
Kikuchi, T., Tanida, S., Otsuki, K., Yasuda, T., and Furusho, J. 2010. Development Of Third Generation Intelligently Controllable Ankle-Foot Orthosis With Compact MR Fluid Brake. IEEE International Conference on Robotics and Automation. 2209-2214.
Kikuchi, T., Tanida, S., Otsuki, K., Yasuda, T., and Furusho, J. 2010. A Novel Estimating Method Of The Gait State And Velocity Control In The Initial Stance Phase For The Intelligent Ankle Foot Orthosis With Compact MR Fluid Brake (i-AFO). Journal of JSEM. 10: 240-246.
Kikuchi, T., Tanida, S., Yasuda, T., and Fujikawa, T. 2013. Development Of Control Model For Intelligently Controllable Ankle-Foot Orthosis. Annual International Conference of the IEEE EMBS. 35: 330-333.
Sanchez-Valdes, D., Alvarez-Alvarez, A., and Trivino, G. 2015. Walking Pattern Classification Using A Granular Linguistic Analysis. Applied Soft Computing. 33: 100-113.
Altas, I. H., and Neyens, J. 2006. A Fuzzy Logic Decision Maker And Controller For Reducing Load Frequency Oscillations In Multi-Area Power Systems. IEEE Power Engineering Society General Meeting. 1-9.
Lee, C. S., and Gonzalez, R. V. 2008. Fuzzy Logic Versus A PID Controller For Position Control Of A Muscle-Like Actuated Arm. Journal of Mechanical Science and Technology. 22(8): 1475-1482.
Potluri, C., Kumar, P., Anugolu, M., Chiu, S., Urfer, A., Schoen, M. P., and Naidu, D. 2010. sEMG Based Fuzzy Control Strategy With ANFIS Path Planning For Prosthetic Hand. IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. 413-418.
Lee, C. S., & Gonzalez, R. V. 2008. Fuzzy Logic Versus A PID Controller For Position Control Of A Muscle-Like Actuated Arm. Journal of Mechanical Science and Technology. 22(8): 1475-1482.
Ubaidillah, N. a., Hudha, K., & Kadir, F. A. A. 2011. Modelling, Characterisation And Force Tracking Control Of A Magnetorheological Damper Under Harmonic Excitation. International Journal of Modelling, Identification and Control. 13(1/2): 9.
Ubaidillah, Imaduddin, F., Nizam, M., & Mazlan, S. a. 2015. Response Of A Magnetorheological Brake Under Inertial Loads. International Journal on Electrical Engineering and Informatics. 7(2): 308-322.
Adiputra, D. 2015. Development of Controller for Passive Control Ankle Foot Orthoses (PICAFO) Based on Electromyography (EMG) Signal and Angle. Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electrical and Chemical Engineering (ICEVT & IMECE). 200-206.
Mahon, C. E., and Lewek, M. D. 2013. Individual Limb Mechanical Analysis Of Gait Following Stroke. Journal of Biomechanics. 1538125(6): 984-989.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.