Robust adaptive neural control of morphing aircraft with prescribed performance
type: Item
instance of: scholarly article
title: Robust adaptive neural control of morphing aircraft with prescribed performance
zbMATH Open document ID: 1426.93153
DOI: 10.1155/2017/1401427
author: Zhonghua Wu
author: Jingchao Lu
author: Jingping Shi
author: Yang Liu
author: Qing Zhou
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93B51
zbMATH DE Number: 6971736
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/1401427
OpenAlex ID: W2614850490
Wikidata QID: Q59147234
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description: scientific article; zbMATH DE number 6971736