Neural networks approximator based robust adaptive controller design of hypersonic flight vehicles systems coupled with stochastic disturbance and dynamic uncertainties
type: Item
instance of: scholarly article
title: Neural networks approximator based robust adaptive controller design of hypersonic flight vehicles systems coupled with stochastic disturbance and dynamic uncertainties
zbMATH Open document ID: 1426.93215
DOI: 10.1155/2017/7864375
author: Ling-Fang Sun
author: Xiuyu Zhang
author: Guo-Qiang Zhu
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: A neural network robust control is proposed for a class of generic hypersonic flight vehicles with uncertain dynamics and stochastic disturbance. Compared with the present schemes of dealing with dynamic uncertainties and stochastic disturbance, the outstanding feature of the proposed scheme is that only one parameter needs to be estimated at each design step, so that the computational burden can be greatly reduced and the designed controller is much simpler. Moreover, by introducing a performance function in controller design, the prespecified transient and performance of tracking error can be guaranteed. It is proved that all signals of closed-loop system are uniformly ultimately bounded. The simulation results are carried out to illustrate effectiveness of the proposed control algorithm.
Mathematics Subject Classification ID: 93C85
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93B35
zbMATH DE Number: 6972498
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/7864375
OpenAlex ID: W2755934258
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description: scientific article; zbMATH DE number 6972498