Adaptive Robust Force Control of an Underactuated Stance Leg Exoskeleton for Human Performance Augmentation

Abstract

Wearable lower limb exoskeleton is a kind of human robot integrated system which can be used to augment the human performance when carrying heavy loads. In recent years, in order to further reduce the self-weight and energy consumption of systems, underactuated exoskeletons are proposed. However, the existed control methods are mainly proposed for fully actuated exoskeletons which are hardly to be applied for underactuated systems. This paper focuses on the precision human machine interaction force control of 3DOF underactuated stance leg exoskeleton system so that accurate human motion trajectory tracking can still be achieved in principle load bearing directions. Specifically, we assume the wearer can provide an appropriate amount of torque to ensure the rotation angle of the exoskeleton back moving in a bounded trajectory. Considering the holonomic constraint provided by the wearer, the 3DOF underactuated exoskeleton system is then transformed into a 2DOF fully actuated system. An adaptive robust cascade force control algorithm is then developed to deal with various parameter uncertainties and uncertain nonlinearities (modeling errors and external disturbances). Comparative Simulation results show that the proposed adaptive robust cascade force control algorithm can achieve high precision human-machine interaction force control in principle load bearing directions, and has strong performance robustness to various model uncertainties.