Predictive control of air-fuel ratio in aircraft engine on fuel-powered unmanned aerial vehicle using fuzzy-RBF neural network
type: Item
instance of: scholarly article
title: Predictive control of air-fuel ratio in aircraft engine on fuel-powered unmanned aerial vehicle using fuzzy-RBF neural network
zbMATH Open document ID: 1448.93232
author: Yixuan Wang
author: Yan Shi
author: Maolin Cai
author: Weiqing Xu
published in: Journal of the Franklin Institute
publication date: 2020-10-07T00:00:00Z
Mathematics Subject Classification ID: 93C85
Mathematics Subject Classification ID: 93C42
zbMATH DE Number: 7257511
zbMATH Keywords: fuel-powered unmanned aerial vehicle
zbMATH Keywords: air-fuel ratio predictive control
zbMATH Keywords: fuzzy-RBF neural network
MaRDI profile type: Publication
full work available at URL: j.jfranklin.2020.03.016
OpenAlex ID: W3016633262
cites work: Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines
cites work: Network-based modelling and dynamic output feedback control for unmanned marine vehicles in network environments
cites work: Networks and the best approximation property
cites work: Optimization of air-fuel ratio control of fuel-powered UAV engine using adaptive fuzzy-PID
DOI: 10.1016/J.JFRANKLIN.2020.03.016
description: scientific article; zbMATH DE number 7257511