The body weight support method of exoskeletons is useful for the rehabilitation of patients with lower limb dyskinesia and performance augmentation of elders. Rather than enhancing the human joint strength directly, the body weight support exoskeleton provides assistance by supporting part of the human body weight and thus releasing the burden experienced by human legs. This paper proposes a two-level hierarchical control method aiming to achieve task-free and strong system robustness. A scale force control method, which can be simply described as making the supporting force of the robot several times the magnitude of a human leg, is developed as the high-level controller. The low-level controller is designed as a fuzzy cerebellar model articulation controller (CMAC)-based adaptive control to enhance the system robustness. The proposed method is applied to a squat movement and a single leg stance movement and is simulated in a combined simulation environment. The results show that the exoskeleton can always provide improved support according to the foot reaction force that is controlled by the user and that the system handles model errors and external disturbances well.

Load-carrying exoskeletons need to cope with load variations, outside disturbances, and other uncertainties. This paper proposes an adaptive trajectory tracking control scheme for the load-carrying exoskeleton. The method is mainly composed of a computed torque controller and a fuzzy cerebellar model articulation controller. The fuzzy cerebellar model articulation controller is used to approximate model inaccuracies and load variations, and the computed torque controller deals with tracking errors. Simulations of an exoskeleton in squatting movements with model parameter changes and load variations are carried out, respectively. The results show a precise tracking response and high uncertainties toleration of the proposed method.