Author(s)

Ming‐Kun Chang

Department of Mechanical and Computer‐Aided Engineering, St. John’s University, New Taipei city.

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.

Adaptive Self‐Tuning Fuzzy Control, Pneumatic Artificial Muscles, Functional Approximation, Lyapunov Function

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.