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

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