Nonlinear model predictive control of an upper extremity rehabilitation robot using a two-dimensional human-robot interaction model

摘要: Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive practice to post-stroke patients, 2) minimize therapist interventions, 3) increase the number of patients per therapist, thereby decreasing associated cost. The control is often limited black-or gray-box approaches; thus, safety issues regarding human-robot interaction not easily considered. To overcome this issue, controllers working with physics-based models gain more importance. In study, we an efficient two dimensional (2D) model implement a model-based controller planar end-effector-type robot. was used within nonlinear predictive (NMPC) structure GPOPS-II optimal package proposed NMPC structure. performance evaluated by simulating system, modeled in MapleSim®. musculoskeletal arm interacting robot predict movement muscle activation patterns, which provide assistance patient. simulations, achieved desired predicted muscular activities dysfunctional subject good accuracy. our future work, exploiting framework will be real-time