FSE-RBFNNs-Based Adaptive Tracking Control of Hypersonic Flight Vehicles with Uncertain Periodic Time-Varying Disturbances

Zehong Dong, Yinghui Li, Renwei Zuo, Haojun Xu, Maolong Lv

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

This work for the first time develops a neuro-adaptive control strategy for an extended class of longitudinal dynamics of hypersonic flight vehicles (HFVs). To handle with the design difficulty that the uncertain time-varying disturbances appear implicitly in HFVs dynamics, a new function approximator is designed by incorporating the fourier series expansion (FSE) into the radial basis function neural networks (RBFNNs). An integral term and a linear term are, respectively, constructed to speed up the convergence rate and compensate for the negative effects caused by approximation errors. It is rigorously proved that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Simulation results verify the effectiveness of the proposed control methodology.

Original languageEnglish
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun Fu, Jian Sun
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages6965-6971
ISBN (Electronic)9789881563903
DOIs
Publication statusPublished - 2020
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: 27 Jul 202030 Jul 2020

Conference

Conference39th Chinese Control Conference, CCC 2020
CountryChina
CityShenyang
Period27/07/2030/07/20

Keywords

  • FSE-RBFNNs-Based Approximator
  • Hypersonic Flight Vehicles
  • Uncertain Periodic Disturbances

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