Active disturbance rejection control of carrier-based aircraft based on offline network/online identification

To address the high-precision landing control problem of carrier-based aircraft under complex environment and strong uncertainty, this paper proposes a direct lift mode active disturbance rejection control method based on offline neural network/online identification. Firstly, referring to the American ‘Magic Carpet’ control system and analyzing its key technical mechanism, the direct lift landing active disturbance rejection control method of carrier-based aircraft is designed. The extended state observer is used to estimate and compensate the total disturbance caused by gust disturbance and system uncertainty. Secondly, according to the evaluation criteria of landing control engineering performance index, the optimal control parameters are selected, and the neural network mapping relationship with flight model uncertainty as input and optimal landing control parameters as output is established. Finally, the active disturbance rejection control parameters are efficiently optimized by online identification. The simulation results show that the proposed method has higher robustness than the baseline controller, and can effectively improve the high-precision landing performance of carrier-based aircraft under interference conditions.