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

Conference paper (2020)

Authors

Zehong Dong Air Force Engineering University China
Yinghui Li Air Force Engineering University China
Renwei Zuo Air Force Engineering University China
Haojun Xu Air Force Engineering University China
Maolong Lv Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
FSE-RBFNNs-Based Approximator Hypersonic Flight Vehicles Uncertain Periodic Disturbances
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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

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.

Files

09189099.pdf
(.pdf | 0.532 Mb)