Attitude control of super-maneuverable aircraft using a wavelet neural network disturbance observer

Abstract

To address system uncertainties encountered during the operation of super-maneuverable aircraft—including modeling errors, external disturbances, and unmodeled dynamics, this paper proposes a robust adaptive control method based on a wavelet neural network disturbance observer (WDO) to achieve robust attitude control. First, a WDO is designed to estimate and compensate for the aforementioned system uncertainties. Then, a control strategy integrating the WDO with trajectory linearization control (TLC) is developed to construct a robust adaptive control system applicable to supermaneuverable aircraft under high-maneuver flight conditions. Finally, the effectiveness of the proposed strategy is verified through comparative numerical simulations. The results demonstrate that the method can effectively suppress system uncertainties and improve the robustness of the attitude control system for super-maneuverable aircraft.