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 language | English |
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Pages (from-to) | 656-663 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 8 |
Issue number | 2 |
Early online date | 15 Dec 2022 |
DOIs | |
Publication status | Published - 1 Feb 2023 |
Keywords
- adaptive control
- Ankle rehabilitation robot
- iterative learning control
- pneumatic muscle