Performance-Based Iterative Learning Control for Task-Oriented Rehabilitation: A Pilot Study in Robot-Assisted Bilateral Training
Qing Miao, Zhijun Li, Kaiya Chu, Yudong Liu, Yuxin Peng, Sheng Quan Xie, Mingming Zhang
Abstract
Active participation from human subjects can enhance the effectiveness of robot-assisted rehabilitation. Developing interactive control strategies for customized assistance is therefore essential for encouraging human–robot engagement. However, existing human–robot interactive control strategies lack precise evaluation indicators with effective convergence method to steadily and rapidly customize appropriate assistance during task-oriented training. This study proposes a performance-based iterative learning control algorithm for robot-assisted training, which aims at providing subject-specific robotic assistance to encourage active participation. Three performance indicators based on a Fugl-Meyer assessment (FMA) regression model are introduced to associate clinical scales with robot-based measures, and a fuzzy logic is employed for comprehensive performance evaluation. To increase efficient training time, a piecewise learning rate-based iterative law is applied to quickly converge to a subject-specific control parameter session by session. The proposed strategy is preliminarily estimated for a case of bilateral upper limb training with an end-effector-based robotic system. The experimental results with human subjects indicate that the proposed strategy can obtain appropriate parameters after only several iterations and adapt to random perturbations (like muscle fatigue).