Data-Driven Adaptive Iterative Learning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

作者：Kun Qian, Zhenhong Li, Zhi-Li Zhang, Guqiang Li, Shengquan Xie · 发表于：IEEE Robotics and Automation Letters · 年份：2023 · DOI：10.1109/lra.2022.3229570 · 被引用次数：23 · 研究领域：Computer Science

This letter investigates the repetitive range of motion (ROM) training control for a compliant ankle rehabilitation robot (CARR). The CARR utilizes four pneumatic muscle (PM) actuators to manipulate the ankle with three rational degree-of-freedoms (DoFs) and soft human-robot interaction, but the strong-nonlinearity of the PM actuator makes precise tracking difficult. To improve the training effectiveness, a data-driven adaptive iterative learning controller (DDAILC) is proposed based on compact form dynamic linearization (CFDL) with estimated pseudo-partial derivative (PPD). Instead of using a PM dynamic model, the estimated PPD is updated merely by online input-output (I/O) measures. Sufficient conditions are established to guarantee the convergence of tracking errors and the boundedness of control input. Experimental studies are conducted on ten human participants with two therapist-resembled trajectories. Compared with other data-driven methods, the proposed DDAILC demonstrates significant improvement on tracking performance.