Volume 16 Number 2
April 2019
Article Contents
Imen Zaidi, Mohamed Chtourou and Mohamed Djemel. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing, vol. 16, no. 2, pp. 213-225, 2019. doi: 10.1007/s11633-017-1062-2
Cite as: Imen Zaidi, Mohamed Chtourou and Mohamed Djemel. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing, vol. 16, no. 2, pp. 213-225, 2019.

# Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm

Author Biography:
• Mohamed Chtourou received the B. Eng. degree in electrical engineering from the National Engineering School of Sfax, Tunisia in 1989, the M. Sc. degree in automatic control from the National Institute of Applied Sciences of Toulouse, France in 1990, and the Ph. D. degree in process engineering from the National Polytechnic Institute of Toulouse, France in 1993. He is currently a professor in the Department of Electrical Engineering, National Engineering School of Sfax, Tunisia. He is the author and co-author of more than fifty papers in international journals and of more than 70 papers published in national and international conferences.
His research interests include learning algorithms, artificial neural networks and their engineering applications, fuzzy systems, and intelligent control.
E-mail: mohamed.chtourou@enis.rnu.tn

Mohamed Djemel received the B. Sc., the M. Sc. and Ph. D. degrees in electrical engineering from the Ecole Supérieure des Sciences Techniques de Tunis, Tunisia in 1987, 1989 and 1996, respectively. He joined the Tunisian University since 1990, where he held different positions involved in research and education. Currently, he is professor of automatic control at the Department of Electrical Engineering of National Engineering School of Sfax, Tunisia. He a member of several national and international conferences.
His research interests include the order reduction, the stability, the control and the advanced control of the complex systems.
E-mail: mohamed.djemel@enis.rnu.tn

• Corresponding author: Imen Zaidi received the B. Eng. degree in electrical engineering, and the M. Sc. degree in automatic and industrial Informatics from the National Engineering School of Sfax, Tunisia in 2007 and 2008, respectively. Currently, she is a Ph. D. degree candidate in the Department of Electrical Engineering, National Engineering School of Sfax, Tunisia.
Her research interests include learning algorithms, artificial neural networks and their engineering applications and intelligent control.
E-mail: zaidi.imen@yahoo.fr (Corresponding author)
ORCID iD: 0000-0002-8873-3596
• Accepted: 2016-08-04
• Published Online: 2017-04-19
• This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system afiected by parametric uncertainties. According to the control scheme, in the flrst step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best conflguration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the efiectiveness of the proposed control strategy.
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures (21)  / Tables (7)

## Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm

• ###### Corresponding author:Imen Zaidi received the B. Eng. degree in electrical engineering, and the M. Sc. degree in automatic and industrial Informatics from the National Engineering School of Sfax, Tunisia in 2007 and 2008, respectively. Currently, she is a Ph. D. degree candidate in the Department of Electrical Engineering, National Engineering School of Sfax, Tunisia. Her research interests include learning algorithms, artificial neural networks and their engineering applications and intelligent control. E-mail: zaidi.imen@yahoo.fr (Corresponding author) ORCID iD: 0000-0002-8873-3596

Abstract: This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system afiected by parametric uncertainties. According to the control scheme, in the flrst step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best conflguration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the efiectiveness of the proposed control strategy.

Imen Zaidi, Mohamed Chtourou and Mohamed Djemel. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing, vol. 16, no. 2, pp. 213-225, 2019. doi: 10.1007/s11633-017-1062-2
 Citation: Imen Zaidi, Mohamed Chtourou and Mohamed Djemel. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing, vol. 16, no. 2, pp. 213-225, 2019.
Reference (42)

/