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

Kun Qian, Zhenhong Li*, Zhiqiang Zhang, Guqiang Li, Sheng Quan Xie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)656-663
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume8
Issue number2
Early online date15 Dec 2022
DOIs
Publication statusPublished - 1 Feb 2023

Keywords

  • adaptive control
  • Ankle rehabilitation robot
  • iterative learning control
  • pneumatic muscle

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