Robust Iterative Feedback Tuning Control of a Compliant Rehabilitation Robot for Repetitive Ankle Training

Abstract

Robot-assisted rehabilitation offers benefits, such as repetitive, intensive, and task-specific training, as compared to traditional manual manipulation performed by physiotherapists. In this paper, a robust iterative feedback tuning (IFT) technique for repetitive training control of a compliant parallel ankle rehabilitation robot is presented. The robot employs four parallel intrinsically compliant pneumatic muscle actuators that mimic skeletal muscles for ankle's motion training. A multiple degrees-of-freedom normalized IFT technique is proposed to increase the controller robustness by obtaining an optimal value for the weighting factor and offering a method with learning capacity to achieve an optimum of the controller parameters. Experiments with human participants were conducted to investigate the robustness as well as to validate the performance of the proposed IFT technique. Results show that the normalized IFT scheme will achieve a better and better tracking performance during the robot repetitive control and provides more robustness to the system by adapting to various situations in robotic rehabilitation.