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Understanding human motion intention is fundamental to wearable robots, which are designed to provide assistance by assessing the intention of wearers. Although modeling human motion intention during physical interaction reveals the fundamental properties of wearable robots, uncertainty and random noise are commonly neglected in existing works. This article presents an adaptive intention-driven variable impedance controller, where the online estimation of human motion intention is realized subject to physical interaction, stochastic distribution, and random disturbance. Specifically, human motion intention is estimated under a dual-channel structure and is represented as both the immediate desired position of the human limb and its predicted future position. A new variable impedance model is formulated from the estimated intention to regulate the dynamic interaction between the human and the robot. Such an impedance model is defined as the control objective achieved using the adaptive controller for wearable compliantly driven robots. The proposed formulation will be able to improve the estimation accuracy of human motion intention and will allow the robot to match the human’s action in a safe and efficient manner. The stability of the closed-loop system is rigorously proven from the stochastic perspective, and the experimental results on the compliantly driven exoskeleton robot are presented to validate the performance of the proposed controller.
Huo et al. (Thu,) studied this question.