Abstract
Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation. Due to the compliance and continuous deformation capacity, rehabilitation devices driven by soft actuators are attached to widespread attention. Considering the large output force of pneumatic artificial muscle (PAM) and the biological musculoskeletal structure, an antagonistic PAM-driven rehabilitation robotic device is developed. To fulfill the need for control of the proposed device, a knowledge-guided data-driven modeling approach is used and an adaptive feedforward–feedback control approach is presented to ensure the motion accuracy under large deformation motion with high frequency. Finally, several simulations and experiments are carried out to evaluate the performance of the developed system, and the results show that the developed system with the proposed controller can achieve expected control performance under various operations.
Highlights
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; Abstract: Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation
The control issues of a rehabilitation robotic device driven by soft pneumatic artificial muscle (PAM) for stroke patients has been investigated
For medical applications, a rehabilitation robotic device driven by two PAMs in antagonistic pairs is designed
Summary
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; Abstract: Stroke is becoming a widely concerned social problem, and robot-assisted devices have made considerable contributions in the training and treatment of rehabilitation. Considering the large output force of pneumatic artificial muscle (PAM) and the biological musculoskeletal structure, an antagonistic PAM-driven rehabilitation robotic device is developed. To fulfill the need for control of the proposed device, a knowledge-guided data-driven modeling approach is used and an adaptive feedforward–feedback control approach is presented to ensure the motion accuracy under large deformation motion with high frequency. The World Health Organization (WHO) describes stroke as “rapidly developing clinical signs of focal (or global) disturbance of cerebral function, with symptoms lasting 24 h or longer or leading to death, with no apparent cause other than of vascular origin” [1]. Stroke has been the second largest cause of death globally after ischemic heart disease, and up to now, the burden of the stroke remains heavy [2]. More than 2/3 patients affected by stroke suffer from impaired upper limb motor function, which brings great inconvenience to daily life [3]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
