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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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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

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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  • 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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    Highlights

    • 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.

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