Artificial neural network-based control of powered knee exoskeletons for lifting tasks: design and experimental validation

• Published in: Robotica Vol. 42; no. 9; pp. 2949 - 2968
• Main Authors: Arefeen, Asif, Xiang, Yujiang
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.09.2024
• Subjects: Age groups; Algorithms; Artificial neural networks; Control methods; Cost function; Design optimization; Exoskeletons; General regression neural networks; Hoisting; Human mechanics; Injuries; Joints (anatomy); Kinematics; Knee; Machine learning; Metabolism; Neural networks; Optimal control; Optimization; Parameters; Quadratic programming; Real time; Regression models; Rehabilitation; Torque; artificial neural network; motion planning; general regression neural network; wearable robots; gradient-based optimization; lifting
• ISSN: 0263-5747; 1469-8668; 1469-8668
• DOI: 10.1017/S0263574724001206

This study introduces a hybrid model that utilizes a model-based optimization method to generate training data and an artificial neural network (ANN)-based learning method to offer real-time exoskeleton support in lifting activities. For the model-based optimization method, the torque of the knee exoskeleton and the optimal lifting motion are predicted utilizing a two-dimensional (2D) human–exoskeleton model. The control points for exoskeleton motor current profiles and human joint angle profiles from cubic B-spline interpolation represent the design variables. Minimizing the square of the normalized human joint torque is considered as the cost function. Subsequently, the lifting optimization problem is tackled using a sequential quadratic programming (SQP) algorithm in sparse nonlinear optimizer (SNOPT). For the learning-based approach, the learning-based control model is trained using the general regression neural network (GRNN). The anthropometric parameters of the human subjects and lifting boundary postures are used as input parameters, while the control points for exoskeleton torque are treated as output parameters. Once trained, the learning-based control model can provide exoskeleton assistive torque in real time for lifting tasks. Two test subjects’ joint angles and ground reaction forces (GRFs) comparisons are presented between the experimental and simulation results. Furthermore, the utilization of exoskeletons significantly reduces activations of the four knee extensor and flexor muscles compared to lifting without the exoskeletons for both subjects. Overall, the learning-based control method can generate assistive torque profiles in real time and faster than the model-based optimal control approach.