Volume 13 Number 3
June 2016
Article Contents
Xiao-Qiang Li, Dan Wang and Zhu-Mu Fu. Adaptive NN Dynamic Surface Control for a Class of Uncertain Non-affine Pure-feedback Systems with Unknown Time-delay. International Journal of Automation and Computing, vol. 13, no. 3, pp. 268-276, 2016. doi: 10.1007/s11633-015-0924-8
Cite as: Xiao-Qiang Li, Dan Wang and Zhu-Mu Fu. Adaptive NN Dynamic Surface Control for a Class of Uncertain Non-affine Pure-feedback Systems with Unknown Time-delay. International Journal of Automation and Computing, vol. 13, no. 3, pp. 268-276, 2016.

# Adaptive NN Dynamic Surface Control for a Class of Uncertain Non-affine Pure-feedback Systems with Unknown Time-delay

Author Biography:
• Dan Wang graduated in automation engineering from Dalian University of Technology, China in 1982. He received the M. Sc. degree in marine automation engineering from Dalian Maritime University, China in 1987, and the Ph.D. degree in automation and computer-aided engineering from Chinese University of Hong Kong, China in 2001. He is currently a professor at Dalian Maritime University, China. His research interests include nonlinear control theory and applications, neural networks, adaptive control, robust control, fault detection and isolation, and system identification. E-mail: dwangdl@gmail.com

Zhu-Mu Fu graduated in mechanical design and manufacturing from Luoyang Institute of Technology, China in 1998. He received the M. Sc. degree in vehicle engineering from Henan University of Science and Technology, China in 2003. He received the Ph. D. degree in control theory and control engineering from Southeast University, China in 2007. He is currently an associate professor at Henan University of Science and Technology, China. His research interests include nonlinear control theory and applications, H-infinity control, energy management strategy for hybrid electric vehicle. E-mail: fzm1974@163.com

• Corresponding author: Xiao-Qiang Li graduated in mathematics from Datong University, China in 2004. He received the M. Sc. degree in mathematics from Dalian Maritime University, China in 2007 and the Ph.D. degree in control theory and control engineering from Dalian Maritime University, China in 2011. He is currently an associate professor at Henan of University Science and Technology, China. His research interests include nonlinear control theory and applications, neural networks, adaptive control. E-mail: sxxqli@126.com (Corresponding author) ORCID iD: 0000-0001-7118-6816
Fund Project:

the Science and Technology Innovative Foundation for Distinguished Young Scholar of Henan Province No. 144100510004

the Science and Technology Programme Foundation for the Innovative Talents of Henan Province University No. 13HASTIT038

the Key Program of Henan Provincial Department of Education No. 13A470254

National Natural Science Foundation of China Nos. 61273137 and 51375145

• Adaptive neural network (NN) dynamic surface control (DSC) is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of "explosion of complexity" and circular construction of the practical controller in the traditional backstepping algorithm are avoided by using this controller design method. For removing the requirements on the sign of the derivative of function fi, Nussbaum control gain technique is used in control design procedure. The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals. Proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Two simulation examples are presented to demonstrate the method.
###### 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 (4)

## Adaptive NN Dynamic Surface Control for a Class of Uncertain Non-affine Pure-feedback Systems with Unknown Time-delay

• ###### Corresponding author:Xiao-Qiang Li graduated in mathematics from Datong University, China in 2004. He received the M. Sc. degree in mathematics from Dalian Maritime University, China in 2007 and the Ph.D. degree in control theory and control engineering from Dalian Maritime University, China in 2011. He is currently an associate professor at Henan of University Science and Technology, China. His research interests include nonlinear control theory and applications, neural networks, adaptive control. E-mail: sxxqli@126.com (Corresponding author) ORCID iD: 0000-0001-7118-6816
Fund Project:

the Science and Technology Innovative Foundation for Distinguished Young Scholar of Henan Province No. 144100510004

the Science and Technology Programme Foundation for the Innovative Talents of Henan Province University No. 13HASTIT038

the Key Program of Henan Provincial Department of Education No. 13A470254

National Natural Science Foundation of China Nos. 61273137 and 51375145

Abstract: Adaptive neural network (NN) dynamic surface control (DSC) is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of "explosion of complexity" and circular construction of the practical controller in the traditional backstepping algorithm are avoided by using this controller design method. For removing the requirements on the sign of the derivative of function fi, Nussbaum control gain technique is used in control design procedure. The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals. Proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Two simulation examples are presented to demonstrate the method.

Xiao-Qiang Li, Dan Wang and Zhu-Mu Fu. Adaptive NN Dynamic Surface Control for a Class of Uncertain Non-affine Pure-feedback Systems with Unknown Time-delay. International Journal of Automation and Computing, vol. 13, no. 3, pp. 268-276, 2016. doi: 10.1007/s11633-015-0924-8
 Citation: Xiao-Qiang Li, Dan Wang and Zhu-Mu Fu. Adaptive NN Dynamic Surface Control for a Class of Uncertain Non-affine Pure-feedback Systems with Unknown Time-delay. International Journal of Automation and Computing, vol. 13, no. 3, pp. 268-276, 2016.
Reference (38)

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