Event-Triggering-Learning-Based ADP Control for Post-Stall Pitching Maneuver of Aircraft.

Area of Science:

• Aerospace Engineering
• Control Systems Theory
• Computational Intelligence

Background:

• Post-stall pitching maneuvers in aircraft present significant control challenges due to unsteady aerodynamic disturbances.
• Existing control methods may struggle with robustness and computational efficiency in these complex flight regimes.

Purpose of the Study:

• To propose an improved event-triggering-learning (ETL)-based adaptive dynamic programming (ADP) method for robust optimal control of aircraft post-stall pitching maneuvers.
• To reduce the computational cost associated with traditional adaptive dynamic programming techniques.
• To enhance control system performance by attenuating aerodynamic disturbances.

Main Methods:

• Design of a feedforward control incorporating a nonlinear disturbance observer (NDO) to mitigate aerodynamic disturbances.
• Application of an adaptive dynamic programming (ADP) approach utilizing a critic neural network to approximate the Hamilton-Jacobi-Bellman value function.
• Integration of an improved event-triggering (ET) mechanism to decrease learning computational cost.

Main Results:

• The proposed ETL-based ADP method achieves robust optimal control for aircraft post-stall pitching maneuvers.
• Significant reduction in computational cost compared to conventional ADP methods.
• Lyapunov stability theory confirms uniform ultimate boundedness of all closed-loop system signals.

Conclusions:

• The developed ETL-based ADP method effectively addresses the challenges of post-stall pitching maneuver control.
• The integration of NDO and ET mechanisms enhances control robustness and computational efficiency.
• Simulation results validate the superior performance of the proposed control strategy.