Passive and Active Control Strategies of a Leg Rehabilitation Exoskeleton Powered by Pneumatic Artificial Muscles

Nerve injury can cause lower limb paralysis and gait disorder. Currently lower limb rehabilitation exoskeleton robots used in the hospitals need more power to correct abnormal motor patterns of stroke patients' legs. These gait rehabilitation robots are powered by cumbersome and bulky electric motors, which provides a poor user experience. A newly developed gait rehabilitation exoskeleton robot actuated by low-cost and lightweight pneumatic artificial muscles (PAMs) is presented in this research. A model-free proxy-based sliding mode control (PSMC) strategy and a model-based chattering mitigation robust variable control (CRVC) strategy were developed and first applied in rehabilitation trainings, respectively. As the dynamic response of PAM due to the compressed air is low, an innovative intention identification control strategy was taken in active trainings by the use of the subject's intention indirectly through the estimation of the interaction force between the subject's leg and the exoskeleton. The proposed intention identification strategy was verified by treadmill-based gait training experiments.