@inproceedings{38bffee443b144749c1fd39f013e3469,
title = "Adaptive neural network PID controller design for temperature control in vacuum thermal tests",
abstract = "Temperature control is important for reliability testing of aerospace products in vacuum thermal environment. The traditional proportional-integral-derivative (PID) controller based closed-loop control system cannot guarantee the high precision requirements in the experiment temperature control. In this paper, an adaptive PID controller based on the radial basis function (RBF) neural network is designed to address the temperature control problem in the thermal vacuum tests. Simulation results show that the designed adaptive closed-loop control system can track the given references with better tracking performance, compared to the conventional PID control method.",
keywords = "RBF neural network, Thermal vacuum test, adaptive control, temperature control",
author = "Haiyang Zhan and Yu Sun and Deyuan Liu and Hao Liu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 28th Chinese Control and Decision Conference, CCDC 2016 ; Conference date: 28-05-2016 Through 30-05-2016",
year = "2016",
month = aug,
day = "3",
doi = "10.1109/CCDC.2016.7531028",
language = "英语",
series = "Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "458--463",
booktitle = "Proceedings of the 28th Chinese Control and Decision Conference, CCDC 2016",
address = "美国",
}