Dynamic recurrent fuzzy neural network-based adaptive sliding control for longitudinal automatic carrier landing system

Abstract

The robustness of automatic carrier landing system(ACLS) is strong because the of the nonlinearity and uncertainty of carrier-based aircraft model in the final-approach. In order to solve the problem, an adaptive sliding mode control based on dynamic recurrent fuzzy neural network is designed to apply in the longitudinal landing system. Firstly, the adaptive sliding mode control method is used to suppress the disturbance of carrier air-wake. Next, the dynamic recurrent fuzzy neural network is applied to compensate the system failure, which further ensures the accurate tracking of ideal glide slop for the carrier-based aircraft. Finally, the effectiveness of the proposed control algorithm is verified by simulation results. Compared with the normal control system of the ACLS, this design can obviously improve the stability of the system and has a good restraint effect on the disturbance of the carrier air-wake.

Keywords

Dynamic recurrent fuzzy neural network adaptive sliding control automatic carrier landing system carrier air-wake ideal glide slop

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