Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults

TLDR

The study develops a fault‑tolerant control strategy for a hypersonic flight vehicle using back‑stepping and composite learning. The controller employs a barrier Lyapunov function to enforce AOA constraints, a robust adaptive allocation law to compensate unknown actuator faults, and composite learning with a serial–parallel estimation model to update neural network weights for uncertainty approximation. Simulations demonstrate that the controller achieves accurate tracking despite AOA constraints and actuator faults.

Abstract

This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial–parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.