Trajectory planning and tracking control for upper limb traction rehabilitation training.
10.7507/1001-5515.202501049
- Author:
Shengguo LUO
1
;
Xiangyun LI
2
;
Qi LU
3
;
Peng CHEN
4
;
Kang LI
2
Author Information
1. College of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China.
2. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China.
3. College of Pittsburgh, Sichuan University, Chengdu 610065, P. R. China.
4. College of Mechanical Engineering, Southwest Jiaotong University, Chendu 610031, P. R. China.
- Publication Type:Journal Article
- Keywords:
Backstepping control;
Interactive space;
Radial basis function neural network;
Singularity avoidance;
Upper limb rehabilitation
- MeSH:
Humans;
Upper Extremity;
Robotics/methods*;
Biomechanical Phenomena;
Neural Networks, Computer;
Traction/methods*;
Algorithms
- From:
Journal of Biomedical Engineering
2025;42(2):318-325
- CountryChina
- Language:Chinese
-
Abstract:
To solve the safety problems caused by the restriction of interaction space and the singular configuration of rehabilitation robot in terminal traction upper limb rehabilitation training, a trajectory planning and tracking control scheme for rehabilitation training is proposed. The human-robot safe interaction space was obtained based on kinematics modeling and rehabilitation theory, and the training trajectory was planned based on the occupational therapy in rehabilitation medicine. The singular configuration of the rehabilitation robot in the interaction space was avoided by exponential adaptive damped least square method. Then, a nonlinear controller for the upper limb rehabilitation robot was designed based on the backstepping control method. Radial basis function neural network was used to approximate the robot model information online to achieve model-free control. The stability of the controller was proved by Lyapunov stability theory. Experimental results demonstrate the effectiveness and superiority of the proposed singular avoidance control scheme.
