Interactive learning for sensitivity factors of a human-powered augmentation lower exoskeleton

Sensitivity Amplification Control (SAC) algorithm was first proposed in the augmentation applications of Berkeley Lower Extremity Exoskeleton (BLEEX). The SAC algorithm is widely used in human augmentation applications since it just need the information from the exoskeleton robot, so that the complexity of exoskeleton system can be reduced greatly. However, the SAC algorithm has two main drawbacks: 1) requiring accurate dynamic models of the exoskeleton, 2) can not manage the variation of interaction dynamics from different walking speed. This paper presents a novel developed learning control strategy based on SAC algorithm. In the proposed Adaptive Sensitivity Amplification Control (ASAC) strategy, the reinforcement learning method is utilized to learn the sensitivity factors online for the sake of handling the variation of interaction dynamics. We demonstrate the control efficiency of ASAC on an one degree-of-freedom (DOF) platform with swing movements first, and then extend it into a HUman-powered Augmentation Lower EXoskeleton (HUALEX). The experimental results show that the proposed ASAC strategy can handle the changing interaction dynamics with less interaction force between the pilot and the exoskeleton as compared with traditional SAC algorithm.