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

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 ex...

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Vydané v:Robotica Ročník 42; číslo 9; s. 2949 - 2968
Hlavní autori: Arefeen, Asif, Xiang, Yujiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cambridge, UK Cambridge University Press 01.09.2024
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0263-5747
1469-8668
DOI:10.1017/S0263574724001206