期刊
IEEE ROBOTICS AND AUTOMATION LETTERS
卷 8, 期 2, 页码 656-663出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3229570
关键词
Ankle rehabilitation robot; pneumatic muscle; iterative learning control; adaptive control
类别
This letter proposes a data-driven adaptive iterative learning controller (DDAILC) for a compliant ankle rehabilitation robot (CARR) with the use of four pneumatic muscle (PM) actuators. The DDAILC utilizes compact form dynamic linearization (CFDL) with estimated pseudo-partial derivative (PPD) to overcome the nonlinearity of the PM actuators. Experimental studies demonstrate significant improvement in tracking performance compared to other data-driven methods.
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.
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