@inproceedings{2a83b4395ddd4c62a18f8be8377625dd,
title = "Improved Firefly Algorithm for Optimization of Aero-engine Controller Based on PIDNN",
abstract = "In this paper, a two-control variable controller based on the Proportional-Integral-Derivative Neural Network (PIDNN) is designed to control a certain type of mixed exhaust turbofan engine. According to the working principle of the aero-engine, a two-variable small deviation state model of the aero-engine is firstly established. Then PIDNN including an input layer, a hidden layer and an output layer is used to design the controller of aero-engine states model. There are 4 nods in input layer, 6 in hide layer and 2 in output layer. To solve the problems of large steady-state error and long adjustment time of the PIDNN controller, this paper uses the improved firefly algorithm to dynamically adjust the initial connection weights of the PIDNN. The results show that the established aero-engine PIDNN controller based on the improved firefly algorithm has the characteristics of short adjustment time and high accuracy, which meets the requirements of aero-engine controller design.",
keywords = "Aero-engine, Improved Firefly Algorithm, PIDNN",
author = "Zongting Jiang and Lingfeng Gou and Chujia Sun and Meng Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 Technical Committee on Control Theory, Chinese Association of Automation.; 40th Chinese Control Conference, CCC 2021 ; Conference date: 26-07-2021 Through 28-07-2021",
year = "2021",
month = jul,
day = "26",
doi = "10.23919/CCC52363.2021.9550688",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7921--7926",
editor = "Chen Peng and Jian Sun",
booktitle = "Proceedings of the 40th Chinese Control Conference, CCC 2021",
}