Predictive control of air-fuel ratio in aircraft engine on fuel-powered unmanned aerial vehicle using fuzzy-RBF neural network

DOI10.1016/j.jfranklin.2020.03.016zbMath1448.93232OpenAlexW3016633262MaRDI QIDQ2005275

Weiqing Xu, Yixuan Wang, Maolin Cai, Yan Shi

Publication date: 7 October 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.03.016

zbMATH Keywords

air-fuel ratio predictive controlfuel-powered unmanned aerial vehiclefuzzy-RBF neural network

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Automated systems (robots, etc.) in control theory (93C85)

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Cites Work

• Networks and the best approximation property
• Optimization of air-fuel ratio control of fuel-powered UAV engine using adaptive fuzzy-PID
• Network-based modelling and dynamic output feedback control for unmanned marine vehicles in network environments
• Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

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