Lower Limb Exoskeleton Hybrid Phase Control Based On Fuzzy Gain Sliding Mode Controller

Exoskeleton robots have been developed to enhance human mobility and reduce the muscle fatigue caused by heavy loads. Now that the exoskeleton should reach transparency with the wearer, the human-machine interaction based control strategy will significantly enhance collaboration between the exoskeleton and the user. This paper proposes a novel method called Hybrid Phase Control (HPC) based on different dynamic models in stance and swing phase of a gait cycle. The dynamic models are derived by using Euler-Lagrange Equation. The proposed method is applied in a lower extremity exoskeleton to track a desired trajectory. In order to improve the adaptive capacity of the controller, we introduce the Sliding Mode Control (SMC) and further eliminate the chattering phenomenon of traditional SMC by using fuzzy logic system (FLS) to adjust SMC switch-gain. The proposed control strategy was proved stable with Lyapunov analysis and used for trajectory tracking in comparison with a conventional sliding mode control. Results with Matlab simulation shows that the proposed controller can achieve better tracking capability and the chattering effect is greatly reduced.