Fixed-time angle of attack constrained control for aircraft considering dynamic icing process.

Area of Science:

• Aerospace Engineering
• Control Systems
• Fluid Dynamics

Background:

• Aircraft icing significantly degrades aerodynamic performance and reduces stall angle of attack.
• Existing control methods often assume instantaneous icing, failing to address the dynamic nature of ice accretion.
• Stabilizing aircraft during icing requires fast convergence of tracking errors.

Purpose of the Study:

• To develop a fixed-time angle of attack-constrained control strategy for aircraft experiencing dynamic icing.
• To investigate the impact of dynamic icing on aerodynamic coefficients.
• To propose a deep neural network-based method for predicting the stall angle of attack under icing conditions.

Main Methods:

• Conducted ice wind tunnel experiments to analyze aerodynamic coefficient variations with angle of attack and icing intensity.
• Employed a fitting method to establish relationships between aerodynamic coefficients and icing parameters.
• Developed a deep neural network for real-time stall angle of attack determination.
• Designed a fixed-time convergent, angle of attack-constrained robust control method.

Main Results:

• Characterized the dynamic icing process of an airfoil through wind tunnel experiments.
• Quantified the relationship between lift, drag, pitching moment coefficients, angle of attack, and icing intensity.
• Demonstrated the effectiveness of the deep neural network in predicting stall angle of attack.
• Simulation results validated the proposed robust control strategy's performance.

Conclusions:

• The developed fixed-time control strategy effectively manages angle of attack constraints during dynamic aircraft icing.
• The study provides a comprehensive understanding of aerodynamic changes during icing and a predictive tool for stall conditions.
• The proposed method enhances aircraft stability and safety in the presence of dynamic icing.