RUPERT closed loop control design.
Rehabilitation robotics is an active area of research in the field of stroke rehabilitation. There is significant potential for improving the current physical rehabilitation methods after stroke through the use of robotic devices. RUPERT is a wearable robotic exoskeleton powered by pneumatic muscle actuators. An adaptive robot control strategy combining a PID-based feedback controller and an Iterative Learning Controller (ILC) is proposed for performing passive reaching tasks. Additionally, a fuzzy rule-base for estimating the learning rate for the ILC is also proposed. The proposed control scheme has the ability to adapt to different subject for performing different reaching tasks. The preliminary results from two able-bodied subjects demonstrate that the proposed controller can provide consistent performance for different subjects performing different reaching tasks.
Balasubramanian
Sivakumar
S
Harrington Department of Bioengineering, Arizona State University, Tempe, 85287, USA.
Wei
Ruihua
R
He
Jiping
J
D000328:Adult; D003198:Computer Simulation; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005081:Exercise Therapy / Q000295:instrumentation* / Q000379:methods; D005246:Feedback; D017143:Fuzzy Logic; D006801:Humans; D008297:Male; D008954:Models, Biological; D009069:Movement Disorders / Q000503:physiopathology* / Q000534:rehabilitation*; D012371:Robotics / Q000295:instrumentation* / Q000379:methods; D020521:Stroke / Q000503:physiopathology*; D000071939:Stroke Rehabilitation; D013813:Therapy, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D034941:Upper Extremity / Q000503:physiopathology*