A Fuzzy Indrect Adaptive Robust Control for Upper Extremity Exoskeleton Driven by Pneumatic Artificial Muscle

Abstract

Pneumatic artificial muscle (PAM), a new type of flexible and high force-to-weight ratio actuator, is widely used in the fields of exoskeleton and bionic robot. Despite these advantages, the dynamics of the flow and pressure as well as the time-varying behavior have increased the nonlinearity of it, which affects the control accuracy of the system significantly. Therefore, high-efficiency and high-precision control algorithms have become the key to improve the motion control performance of PAM. In this paper, a new fuzzy indirect adaptive robust control algorithm (FIARC) is proposed for an upper extremity exoskeleton driven by PAM. The FIARC introduces a modified fuzzy radial basis function (RBF) and a fuzzy control logic integrated with indirect adaptive robust control (IARC) to reduce the control system error effectively. The modified fuzzy RBF is used to compensate the model uncertainty and the deviation of true control paraments, while the fuzzy control logic is used for real-time flow coefficient correction, which combines the flow hysteresis characteristics of the high speed on/off valve with the inlet and exhaust flow model of pneumatic muscle. To verify the performance of the proposed controller, the FIARC is applied to the motion control of elbow joint in the upper extremity exoskeleton. Experimental results show that the FIARC is more advanced and effective in tracking the reference signals compared with conventional method.