Experimental Validation of an Exoskeleton Using Stable Combined Adaptive Interval Type-2 Fuzzy Control

Improving walking is related to biopsychosocial function enhancement for promoting community health. Lower-limb exoskeletons increasingly support and enhance the walking and functionality of patients with mobility impairments. Accurate gait tracking is essential during the rehabilitation training. However, this aim faces challenges due to the uncertainties inherent in the nonlinear dynamics of exoskeleton systems, especially when interacting with the unpredictable behavior of humans. This study suggests modified adaptive laws based on tracking errors and their higher-order derivatives with an interval type-2 fuzzy control to enhance estimation accuracy. Then, we propose a Lyapunov function to derive the introduced composite adaptive laws scheme and ensure stability for these human-worn robots. Experimental tests on a hip exoskeleton validate the effectiveness of our method. The proposed strategy considerably lowers tracking errors by more than 67% compared to a competing framework.