Predefined-Time Hierarchical Coordinated Neural Control for Hypersonic Reentry Vehicle.

Area of Science:

• Aerospace Engineering
• Control Systems Theory
• Nonlinear Control

Background:

• Hypersonic vehicles face challenges due to low actuator efficiency and complex dynamics.
• Coordinated control of multiple control surfaces is crucial for stability and maneuverability.
• Existing control methods may not guarantee performance within a predefined time.

Purpose of the Study:

• To develop a predefined-time hierarchical coordinated adaptive control strategy.
• To address the limitations of low actuator efficiency in hypersonic reentry vehicles.
• To ensure robust tracking performance and system stability.

Main Methods:

• A hierarchical control design coordinating elevator and aileron deflections.
• Predefined-time control laws (equivalent and switching) for system stabilization.
• Composite learning using tracking and prediction errors for uncertainty approximation.
• Lyapunov stability analysis to guarantee bounded tracking errors.

Main Results:

• The proposed control scheme effectively compensates for low actuator efficiency.
• Uniformly ultimately bounded tracking errors are achieved within a predefined time.
• The composite learning approach accurately approximates vehicle dynamics.
• Simulation results validate the tracking performance and learning accuracy.

Conclusions:

• The developed predefined-time hierarchical coordinated adaptive control is effective for hypersonic reentry vehicles.
• The method ensures stability and precise trajectory tracking despite actuator limitations.
• The composite learning mechanism enhances control accuracy and robustness.