Nonaffine control of helicopter engine failure based on adaptive deep belief network

This research presents a nonaffine intelligent control approach based on an adaptive deep belief network (ADBN) to solve the significant nonlinearity, nonaffine features, and parameter uncertainties displayed by helicopters during power-failure scenarios. First, a nominal six-degree-of-freedom rigid-body model of the helicopter is established, and the corresponding nonaffine dynamic model under power failure is derived. The model representation capacity and generalization performance are then improved by using the ADBN to approximate unknown nonlinear factors and a state observer to do state estimation and control. On this basis, a nonaffine controller is constructed in conjunction with adaptive laws to improve the robustness of the system against uncertainties and external disturbances. Finally, simulation results are provided to verify the effectiveness and superiority of the proposed method. The results demonstrate that the proposed control scheme is capable of maintaining satisfactory attitude stability, velocity regulation, and trajectory tracking accuracy in the presence of unknown parameters and environmental disturbances. The proposed approach offers a new technical solution for the safe control of helicopters operating under extreme conditions.