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Adaptive Self‐Tuning Fuzzy Controller for a Soft Rehabilitation Machine Actuated by Pneumatic Artificial Muscles

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DOI: 10.4236/ojapps.2015.55020    2,791 Downloads   3,133 Views   Citations


Pneumatic artificial muscles (PAMs) have the highest power to weight and power to volume ratios of any actuator. Therefore, they can be used not only in rehabilitation engineering, but also as actuators in robots, including industrial and therapy robots. Because PAMs have highly nonlinear and time‐varying behavior associated with gas compression and the nonlinear elasticity of bladder containers, achieving excellent tracking performance using classical controllers is difficult. An adaptive self‐tuning fuzzy controller (ASTFC) including adaptive fuzzy sliding mode control (AFSMC) and functional approximation (FA) was developed in this study for overcoming the aforementioned problems. The FA technique was used to release the model‐based requirements and the update laws for the coefficients of the Fourier series function parameters were derived using a Lyapunov function to guarantee control system stability. The experimental results verified that the proposed approach can achieve excellent control performance despite external disturbance.

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The authors declare no conflicts of interest.

Cite this paper

Chang, M. (2015) Adaptive Self‐Tuning Fuzzy Controller for a Soft Rehabilitation Machine Actuated by Pneumatic Artificial Muscles. Open Journal of Applied Sciences, 5, 199-211. doi: 10.4236/ojapps.2015.55020.


[1] Caldwell, D.G., Medrano-Cerda, G.A. and Goodwin, M. (1995) Control of Pneumatic Muscle Actuator. IEEE Control System Maganize, 15, 40-48.
[2] Chou, C.P. and Hannaford, B. (1996) Measurement and Modeling of Mckibben Pneumatic Artificial Muscles, IEEE Transactions on Robotics and Automation, 12, 90-102.
[3] Xie, S.Q. and Jamwal, P.K. (2011) An Iterative Fuzzy Controller for Pneumatic Muscle Driven Rehabilitation Robot. Expert Systems with Applications, 30, 8128-8137.
[4] Anh, H.P.H. (2010) Online Tuning Gain Scheduling MIMO Neural PID Control of the 2-Axespneumatic Artificial Muscle (PAM) Robot Arm. Expert Systems with Applications, 37, 6547-6560.
[5] Lilly, J.H. and Yang, L. (2005) Sliding Mode Tracking for Pneumaticmuscle Actuators in Opposing Pair Configuration. IEEE Transactions on Control Systems Technology, 4, 550-557.
[6] Ahn, K.K. and Anh, H.P.H. (2010) Inverse Double NARX Fuzzy Modeling for System Identification. IEEE/ASME Transactions on Mechatronics, 15, 136-148.
[7] Seddiki, L., Guelton, K. and Zaytoon, J. (2009) Concept and Takagi-Sugeno Descriptor Tracking Controller Design of a Closed Muscular Chain Lower-Limb Rehabilitation Device. IET Control Theory and Applications, 4, 1407-1420.
[8] Chen, H.Y. and Huang, S.J. (2008) A New Model-Free Adaptive Sliding Controller for Active Suspension System, International Journal of System Science, 39, 57-69.
[9] Palm, R. (1994) Robust Control by Fuzzy Sliding Mode. Automatica, 30, 1429-1437.
[10] Lee, H., Kim, E., Kang, H.J. and Park, M. (2001) A New Sliding-Mode Control with Fuzzy Boundary Layer Fuzzy Sets System, 120, 135-143.
[11] Lilly, J.H. and Quesada, P.M. (2004) A Two-Input Sliding-Mode Controller for a Planar Arm Actuated by Four Pneumatic Muscle Groups. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 12, 349-358.
[12] Tondu, B. and Lopez, P. (2000) McKibben Artificial Muscle Robot Actuators. IEEE Control Systems Magazine, 20, 15-38.
[13] Narendra, K.S. and Annaswamy, A.M. (1989) Stable Adaptive Systems. Prentice Hall, Englewood Cliffs.

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