To address the problem of accurate landing control of carrier-based aircraft with multiple control surfaces under complex environmental interference conditions, a dynamic compound control method based on model predictive control and incremental dynamic inverse of prescribed performance is proposed. Firstly, combined with the direct lift control strategy, the equivalent error models are established using the idea of prescribed performance. By applying a spatial equivalence transformation, the tracking constraint problem of flight path angle and angle of attack is mapped to the conversion error boundness problem. Secondly, the control law of flight path angle tracking loop and angle of attack holding loop is designed based on incremental nonlinear dynamic inversion, and the control law designed with equivalent error model does not contain the model state feedback items, thereby reducing the dependence of the control law on the exact model. Then, the model predictive control strategy is introduced to realize the compound allocation of direct lift/pitching moment on multiple control surfaces. The dynamic allocation of control commands is realized by rolling optimization in the finite predictive time domain, considering the control performance, rudder surface constraint and rudder dynamic characteristics. Finally, simulation results show that the proposed method can effectively improve the robustness and accuracy of multi-control surface carrier landing control.

This paper investigates the robust adaptive control problem for high-speed morphing aircraft under model uncertainties, external disturbances, and non-minimum phase characteristics. First, a dynamic model incorporating uncertainties is established and subsequently decomposed into velocity and attitude subsystems based on system characteristics. A modified forgetting factor-based online parameter identification method is designed to estimate aerodynamic parameters in real-time, reducing reliance on prior model knowledge while providing real-time mode evaluation information for controller design. Subsequently, a game-enhanced neural network observer is proposed to handle composite disturbances, including identification errors, morphing uncertainties, and external disturbances, ensuring finite-time convergence of disturbance estimation errors to zero. By developing a morphing-redefinition strategy to reconfigure system outputs, specifically defined reference commands are generated for morphing configurations to avoid unstable internal dynamics caused by the coupling between elevators and lift. Finally, system stability is rigorously analyzed using Lyapunov theory, with simulation results validating the effectiveness of the proposed methodology.

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.