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Neural Network Based Adaptive Fuzzy PID-type Sliding Mode Attitude Control for a Reentry Vehicle

Zhen Jin, Jiabin Chen*, Yongzhi Sheng, and Xiangdong Liu
International Journal of Control, Automation, and Systems, vol. 15, no. 1, pp.404-415, 2017

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."

Keyword : "Actuator saturation, adaptive fuzzy PID-type SMC, attitude control, radial basis function neural network, reentry vehicle."

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