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Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation

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Abstract

A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network (ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.

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Authors and Affiliations

  1. School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Shi-tie Zhao  (赵石铁) & Xian-wen Gao  (高宪文)

Authors
  1. Shi-tie Zhao  (赵石铁)
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  2. Xian-wen Gao  (高宪文)
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Corresponding author

Correspondence to Xian-wen Gao  (高宪文).

Additional information

Foundation item: Project(61433004) suppouted by the National Natural Science Foundation of China

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Zhao, St., Gao, Xw. Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation. J. Cent. South Univ. 23, 636–643 (2016). https://doi.org/10.1007/s11771-016-3109-x

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  • DOI: https://doi.org/10.1007/s11771-016-3109-x

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