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Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2019-03-01 , DOI: 10.1109/tcyb.2018.2794972
Bin Xu , Zhongke Shi , Fuchun Sun , Wei He

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.

中文翻译:

基于屏障李雅普诺夫函数的具有AOA约束和执行器故障的高超音速飞行器学习控制

本文研究了利用反步和复合学习对高超音速飞行器的容错控制。考虑到超燃冲压发动机引起的迎角(AOA)约束,基于障碍Lyapunov函数设计了控制律。为了解决未知的执行器故障,提出了一种鲁棒的自适应分配定律来提供补偿。同时,为了获得良好的系统不确定性逼近,通过构造串行-并行估计模型以获得可动态指示智能逼近如何工作的预测误差,提出了一种用于学习神经权重的复合学习方法。仿真结果表明,在存在AOA约束和执行器故障的情况下,该控制器具有良好的系统跟踪性能。
更新日期:2019-03-01
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