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

Bin Xu, Zhongke Shi, Fuchun Sun, Wei He

Research output: Contribution to journalArticlepeer-review

221 Scopus citations

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.

Original languageEnglish
Article number8295169
Pages (from-to)1047-1057
Number of pages11
JournalIEEE Transactions on Cybernetics
Volume49
Issue number3
DOIs
StatePublished - Mar 2019

Keywords

  • Angle of attack (AOA) constraint
  • barrier Lyapunov function (BLF)
  • composite learning
  • fault-tolerant control (FTC)
  • hypersonic flight vehicle (HFV)

Fingerprint

Dive into the research topics of 'Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle with AOA Constraint and Actuator Faults'. Together they form a unique fingerprint.

Cite this