A novel zero-force control framework for post-stroke rehabilitation training based on fuzzy-PID method

As the number of people with neurological disorders increases, movement rehabilitation becomes progressively important, especially the active rehabilitation training, which has been demonstrated as a promising solution for improving the neural plasticity. In this paper, we developed a 5-degree-of-freedom rehabilitation robot and proposed a zero-force control framework for active rehabilitation training based on the kinematics and dynamics identification. According to the robot motion characteristics, the fuzzy PID algorithm was designed to further improve the flexibility of the robot. Experiments demonstrated that the proposed control method reduced the Root Mean Square Error and Mean Absolute Error evaluation indexes by more than 15% on average and improves the coefficient of determination ($$ R^{2} $$ ) by 4% compared with the traditional PID algorithm. In order to improve the active participation of the post-stroke rehabilitation training, this paper designed an active rehabilitation training scheme based on gamified scenarios, which further enhanced the efficiency of rehabilitation training by means of visual feedback.