Adaptive Position Constrained Assist-as-Needed Control for Rehabilitation Robots
Yu Cao, Xinkai Chen, Mengshi Zhang, Jian Huang
Abstract
In rehabilitation practice, motivating patients with neurological injuries to actively increase muscle activity and ensure their safety are important. Therefore, this study proposed a position-constrained assist-as-needed (AAN) control method for rehabilitation robots. A human-robot interaction system with position constraints was first established based on prescribed performance. Aiming at implementing the AAN strategy, the robot assistance level metric (RALM), a constructed global continuous differentiable function incorporating dead-zone and saturation characteristics, was introduced to quantify the robotic assistance and facilitate seamless operation. To bridge the gap between the position constraints and the AAN strategy, a sliding manifold was constructed for the constrained human-robot dynamic system, where RALM was regarded as a weight factor to achieve a human-dominated mode, a robot-dominated mode, and their smooth transition, regarded as a human-robot shared mode. The stability of the closed-loop system was guaranteed by using the Lyapunov theory, and the proposed controller was verified by several physical experiments on a knee exoskeleton driven by pneumatic muscles (PMs).