Paper Info
Title
Robust Online Adaptive Neural Network Control For The Regulation Of Treadmill Exercises
Abstract
The paper proposes a robust online adaptive neural network control scheme for an automated treadmill system. The proposed control scheme is based on Feedback-Error Learning Approach (FELA), by using which the plant Jacobian calculation problem is avoided. Modification of the learning algorithm is proposed to solve the overtraining issue, guaranteeing to system stability and system convergence. As an adaptive neural network controller can adapt itself to deal with system uncertainties and external disturbances, this scheme is very suitable for treadmill exercise regulation when the model of the exerciser is unknown or inaccurate. In this study, exercise intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient in order to achieve fast tracking for which a single input multi output (SIMO) adaptive neural network controller has been designed. Real-time experiment result confirms that robust performance for nonlinear multivariable system under model uncertainties and unknown external disturbances can indeed be achieved.
Year
DOI
Venue
2011
10.1109/IEMBS.2011.6090233
2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
robustness,adaptive system,neural networks,adaptive systems,robust control,control systems,real time,real time systems,neural network
Convergence (routing),Multivariable calculus,Nonlinear system,Jacobian matrix and determinant,Adaptive system,Control theory,Computer science,Robustness (computer science),Control engineering,Control system,Artificial neural network
Conference
Volume
ISSN
Citations 
2011
1557-170X
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Tuan Nghia Nguyen1252.59
Hung T. Nguyen237256.85
Steven W. Su321045.84
Branko G. Celler450281.99