Data-Driven Adaptive Iterative Learning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training
Kun Qian, Zhenghong Li, Zhiqiang Zhang, Guqiang Li, Sheng Quan Xie
Abstract
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.