Name
Title | ||
|---|---|---|
Development of an Intention-Based Adaptive Neural Cooperative Control Strategy for Upper-Limb Robotic Rehabilitation |
Abstract | ||
|---|---|---|
Robotic rehabilitation therapy has become an important technology to recover the motor ability of disabled individuals. Clinical studies indicate that involving the active intention of patient into rehabilitation training contributes to promoting the performance of therapies. An adaptive neural cooperative control strategy is developed in this letter to realize intention-based human-cooperative rehabilitation training. The human motion intention is estimated by fusing the human-robot interaction forces and the muscular forces into a Gaussian radial basis function network. A biological force estimation method is proposed to obtain the muscular forces of biceps and triceps based on surface electromyography signals and Kalman filter. A robust adaptive sliding mode controller is integrated into the cooperative control scheme to ensure the accuracy and stability of inner position control loop with uncertainties. The minimum jerk cost principle is used to improve the smoothness and continuity of trajectory. To evaluate the effectiveness of the proposed control scheme, further experimental investigations are conducted on a planar upper-limb rehabilitation robot with ten volunteers. The results indicate that the proposed control strategy has significant potential to modulate the interaction compliance and cooperation process during training. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2020.3043197 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
Cooperative control,intention recognition,Kalman filter,rehabilitation robotics | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 1 |
PageRank | References | Authors |
0.35 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingcong Wu | 1 | 11 | 5.02 |
| Ying Chen | 2 | 1 | 0.35 |