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Adaptive Control based on Extended Neural Network for SISO Uncertain Nonlinear Systems

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  • Control Theory and Applications
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Abstract

This paper proposes a novel adaptive control criterion for a class of single-input-single-output (SISO) uncertain nonlinear systems by using extended neural networks (ENNs). Distinguished from the traditional neural networks, our ENNs are composed of radial basis function neural networks (RBFNNs), scalers and saturators. And these ENNs are used to approximate the uncertainties in the nonlinear systems. Based on the Lyapunov stability theory and our ENNs, an adaptive control scheme is designed to guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). It is also worth pointing out that our control method makes the construction of RBFNNs and the design of adaptive laws separated, which means only the outputs of ENNs and one update law of the parameter in the scaler are to be adjusted. Thus, our control scheme can effectively reduce the online computation burden of the adaptive parameters. Finally, simulation examples are given to verify the effectiveness of our theoretical result.

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Author information

Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou Higher Education Mega Center, Panyu District, Guangzhou, 510006, China

    Hao-guang Chen, Yin-he Wang & Li-li Zhang

Authors
  1. Hao-guang Chen
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  2. Yin-he Wang
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  3. Li-li Zhang
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Corresponding author

Correspondence to Hao-guang Chen.

Additional information

Recommended by Associate Editor Sing Kiong Nguang Editor Hamid Reza Karimi. This work was supported by the National Natural Science Foundation of China (61273219, 61673120, 61603098), the Natural Science Foundation of Guangdong Province of China (2016A030310336) and Specialized Research Fund for the Doctoral Program of Higher Education of China (20134420110003).

Hao-guang Chen is currently working toward a Ph.D. degree in the School of Automation, Guangdong University of Technology, Guangzhou, P. R. China. His research interests include nonlinear dynamical systems, neural network, fuzzy logic systems and adaptive control.

Yin-he Wang received his M. S. degree in mathematics from Sichuan Normal University, Chengdu, P. R. China, in 1990, and the Ph.D. degree in control theory and engineering from Northeastern University, Shenyang, P. R. China, in 1999. From 2000 to 2002, he was a Post-doctor in Department of Automatic control, Northwestern Polytechnic University, Xi’an, P. R. China. From 2005 to 2006, he was a visiting scholar at Department of Electrical Engineering, Lakehead University, Canada. He is currently a Professor with the Faculty of Automation, Guangdong University of Technology, Guangzhou, China. His research interests include fuzzy adaptive robust control, analysis for nonlinear systems and complex dynamical networks.

Li-li Zhang received the M.S. degree in applied mathematics from University of Science and Technology Beijing, Beijing, P. R. China, in 2004, and the Ph.D. degree in school of automation, Guangdong University of Technology, Guangzhou, China, in 2014. She is currently an associate professor in School of Applied Mathematics, Guangdong University of Technology, Guangzhou, China. Her research interests include control and synchronization for complex dynamical networks and chaotic systems.

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Chen, Hg., Wang, Yh. & Zhang, Ll. Adaptive Control based on Extended Neural Network for SISO Uncertain Nonlinear Systems. Int. J. Control Autom. Syst. 16, 27–38 (2018). https://doi.org/10.1007/s12555-016-0721-3

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  • DOI: https://doi.org/10.1007/s12555-016-0721-3

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