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Neural network based adaptive fuzzy PID-type sliding mode attitude control for a reentry vehicle

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Abstract

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and external disturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilization of radial basis function (RBF) neural network. In order to improve the transient performance and ensure small steady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by using adaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loop system is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term of sliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of a priori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbance information. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numerical simulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving the transient performance and robustness.

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Authors and Affiliations

  1. School of Automation, Key laboratory for Intelligent Control & Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China

    Zhen Jin, Jiabin Chen, Yongzhi Sheng & Xiangdong Liu

Authors
  1. Zhen Jin
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  2. Jiabin Chen
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  3. Yongzhi Sheng
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  4. Xiangdong Liu
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Corresponding author

Correspondence to Jiabin Chen.

Additional information

Recommended by Associate Editor Yingmin Jia under the direction of Editor Euntai Kim. This work is supported by National Natural Science Foundation of China under grant 11402020 and 11372034, Innovative Research Team of Beijing Institute of Technology.

Zhen Jin received the B.S. degree in Control Science and Engineering from Beijing University of Chemistry Technology (BUCT) in 2013. He is now a M.S. candidate at the School of Automation at BIT. His research interests include nonlinear control, adaptive control, sliding mode control.

Jiabin Chen received his Ph.D. degrees from Shanghai Jiaotong University in 1992. He is currently a professor with the School of Automation in BIT. His research interests include high-precision servo control, spacecraft attitude control.

Yongzhi Sheng received his B.S. and M.S. degrees from Beihang University, in 2003 and 2006, respectively, and his Ph.D. degree from the Graduate School of the Second Academy of China Aero-space in 2009. From 2009 to 2011 he did his post doctor research in Beihang Uni-versity. He is currently a lecturer with BIT. His research interests include guid-ance and control design of reentry vehicle.

Xiangdong Liu received his M.S. and Ph.D. degrees from Harbin Institute of Technology (HIT), in 1992 and 1995, respectively. From 1998 to 2000, he did his post doctor research in mechanical postdoctoral research center in HIT. He is currently a professor with the School of Automation in BIT. His research interests include high-precision servo control, spacecraft attitude control, chaos theory.

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Jin, Z., Chen, J., Sheng, Y. et al. Neural network based adaptive fuzzy PID-type sliding mode attitude control for a reentry vehicle. Int. J. Control Autom. Syst. 15, 404–415 (2017). https://doi.org/10.1007/s12555-015-0181-1

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  • DOI: https://doi.org/10.1007/s12555-015-0181-1

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