To address the problem of rapid and stable recovery from deep stall in fighter aircraft, a finite-time control method based on forced oscillation is proposed. To characterize the dynamics of deep stall state, bifurcation theory is employed for analysis, and the region of attraction boundaries is determined through backward-time integration. To generate precise recovery commands from mechanistic perspective, an extended bifurcation analysis is conducted, and forced oscillation commands corresponding to unstable bifurcation points are incorporated into the controller design. To handle time-varying disturbances and aerodynamic parameter perturbations during deep stall, a disturbance observer is designed to estimate lumped uncertainties while neural networks compensate for model uncertainties, and the deep stall recovery controller is obtained by combining with angle of attack tracking error feedback. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation results show that the proposed method can reduce the fighter aircraft's angle of attack to a safe zone while maintaining stable controllability, and achieve rapid and smooth recovery from deep stall conditions.

This paper presents a hierarchical formation control strategy to address the challenges of multiple Unmanned Aerial Vehicles (UAVs) formation control within a cooperative consensus framework. The proposed strategy incorporates a reference command generation layer, which derives UAV attitude commands based on formation requirements, and a tracking control layer to ensure accurate execution. Collaborative variables, including trajectory position and flight speed, are defined using a three-dimensional track particle and autopilot model, enabling the development of a consensus-based formation control law. Desired attitude angles are computed through altitude-hold and coordinated-turn strategies. A sliding surface is designed based on reference models derived from flight quality metrics, while an adaptive controller compensates for aerodynamic model uncertainties. To enhance learning capabilities, a prediction error mechanism based on a series–parallel estimation model is introduced, enabling collaborative learning and the sharing of network weight estimation parameters within the multi-agent system. This facilitates the design of a distributed composite learning law. Lyapunov stability analysis confirms the local exponential stability of the tracking error. The simulations of a twelve-UAV formation, along with comparative analysis of two algorithms, demonstrate the system’s capability for formation maintenance and high-precision tracking control.