A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

Yanhong Luo; Shengnan Zhao; Dongsheng Yang; Huaguang Zhang

doi:10.1109/JAS.2019.1911798

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(1): 48-56

Yanhong Luo, Shengnan Zhao, Dongsheng Yang and Huaguang Zhang, "A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 48-56, Jan. 2020. doi: 10.1109/JAS.2019.1911798

Citation:

Yanhong Luo, Shengnan Zhao, Dongsheng Yang and Huaguang Zhang, "A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 48-56, Jan. 2020. doi: 10.1109/JAS.2019.1911798

Citation:

Yanhong Luo, Shengnan Zhao, Dongsheng Yang and Huaguang Zhang, "A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 48-56, Jan. 2020. doi: 10.1109/JAS.2019.1911798

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A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

doi: 10.1109/JAS.2019.1911798

Funds: This work was supported in part by the National Natural Science Foundation of China (61433004, 61703289)

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Author Bio:
Yanhong Luo (M’09) received the B.S. degree in automation control, the M.S. degree and the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2003, 2006 and 2009, respectively. She is currently with Northeastern University as an Associate Professor. Her research interests include approximate dynamic programming, neural networks adaptive control, and fuzzy control and their industrial application

Shengnan Zhao received the B.S. degree from China University of Mining and Technology, Xuzhou, China, in 2017. She is currently pursuing the M.S. degree with the College of Information Science and Engineering, Northeastern University, Shenyang, China. Her current research interests include neural networks adaptive control and backstepping control

Dongsheng Yang (M’16) received the B.S degree, the M.S degree, and the Ph. D degree from Northeastern University, Shenyang, China, in 1999, 2004, and 2007, respectively. Since 2004, he has been with Northeastern University. His research interests include fuzzy control and fuzzy systems, chaotic control, and complex systems

Huaguang Zhang (SM’04) received the B.S. degree and the M.S. degree in control engineering from Northeast Dianli University of China, Jilin, China, in 1982 and 1985, respectively, and the Ph.D. degree in thermal power engineering and automation from Southeast University, Nanjing, China, in 1991. His research interests include control, stochastic system control, neural networks based control, adaptive dynamic programming, nonlinear control, and their applications. Dr. Zhang was awarded the Outstanding Youth Science Foundation Award from the National Natural Science Foundation Committee of China in 2003 and was named the Cheung Kong Scholar by the Education Ministry of China in 2005. He is an Associate Editor of Automatica, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Cybernetics, and Neurocomputing
Corresponding author: The authors are with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, and also with the School of Information Science and Engineering, Northeastern University, Shenyang 110004, China (e-mail: luoyanhong@ise.neu.edu.cn; 840798763@qq.com; yangdongsheng@mail.neu.edu.cn; hgzhang@ieee.org)
Received Date: 2019-08-21
Accepted Date: 2019-10-24

Abstract

Abstract

For a single machine infinite power system with thyristor controlled series compensation (TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network (RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness (UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

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References(40)

References

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It is the first time that the RBFNN is introduced to the robust adaptive backstepping control scheme for the single machine infinite power system with TCSC, which is applied to approximate complex nonlinear functions including system model uncertainty, nonlinear time-delay and external unknown disturbance without knowing the upper bound of the disturbance and uncertainty terms.
An online updating robust term is proposed to reduce the residual error of the system to ensure the uniform ultimate boundedness of all the weight parameters and states of the whole closed-loop system without knowing the upper bound of the adaptive parameter. The ideal weights of neural networks and the adjusting rule of the adaptive parameter can also be updated online.
Introducing inequalities $0 \le \left| x \right| - x\tanh (x/\varsigma ) \le 0.2785\varsigma $, for $\varsigma > 0$, avoids the appearance of chattering and obtains a smooth robust adaptive control law.

A New Robust Adaptive Neural Network Backstepping Control for Single Machine Infinite Power System With TCSC

doi: 10.1109/JAS.2019.1911798

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