Neural Backstepping Output-Constrained Control for Flexible Civil Aircraft Overload Tracking

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Ride quality and flight safety are critical performance indicators for flexible civil aircraft, particularly under atmospheric disturbances such as gusts and turbulence. Normal overload tracking must satisfy stringent comfort and safety standards defined by ISO 2631-1 and MIL-F-9490D. This paper proposes a neural backstepping output-constrained control strategy to ensure accurate overload tracking while strictly respecting ride quality constraints.

Normal Overload Constraints and Control Objectives

The normal overload constraint arises from the need to limit excessive accelerations that degrade passenger comfort and structural integrity. To explicitly address these constraints, the control design incorporates an integral barrier Lyapunov function (IBLF), which guarantees that the overload response remains within a predefined safe interval throughout system operation.

Neural Backstepping Control Framework

A backstepping-based control architecture is developed for the flexible aircraft model, enabling systematic handling of nonlinear dynamics and output constraints. The IBLF-based control laws ensure constraint satisfaction while maintaining stable overload tracking performance, even in the presence of flexible-body effects inherent in civil aircraft structures.

Handling Model Uncertainty and External Disturbances

To address modeling uncertainties and unknown external disturbances, neural networks are embedded within the control framework to approximate uncertain nonlinearities. In parallel, a disturbance observer is employed to estimate and compensate for external disturbances. A composite learning strategy is further introduced to enhance neural network learning efficiency and convergence accuracy.

Stability and Constraint Satisfaction Analysis

Lyapunov stability theory is used to rigorously prove the uniformly ultimate boundedness of all closed-loop system signals. The analysis also confirms that the normal overload remains strictly within the prescribed bounds imposed by ride quality requirements, ensuring both theoretical soundness and practical reliability of the proposed controller.

Simulation and Hardware-in-the-Loop Validation

Numerical simulations and hardware-in-the-loop (HIL) experiments were conducted under typical discrete gusts and atmospheric turbulence conditions. Results demonstrate that the proposed controller significantly improves ride quality, effectively suppresses overload fluctuations, and consistently maintains the normal overload within the predefined safety interval, confirming its suitability for real-world civil aircraft applications.

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#AerospaceEngineering
#FlexibleAircraft
#NonlinearControl
#LyapunovStability
#DisturbanceObserver
#CompositeLearning
#HILSimulation
#AircraftDynamics
#ControlSystems
#AviationSafety
#IntelligentControl
#EngineeringResearch
#AdvancedFlightControl


 

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