Open AccessNeural Network based Identification and Self-tuning PID Control for Spacecraft Thermal Vacuum Tests
Author(s) -
Dongliang Wu,
Xi Zhu,
Jinyu Wen,
Lin Zhu,
Feng Yao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2216/1/012058
Subject(s) - spacecraft , pid controller , artificial neural network , identification (biology) , control theory (sociology) , temperature control , thermal , control engineering , computer science , control (management) , controller (irrigation) , engineering , physics , aerospace engineering , artificial intelligence , agronomy , botany , meteorology , biology
This paper deals with the high-performance temperature control issues of spacecraft thermal vacuum tests. More precisely, the novel model identification and self-tuning PID control method is developed based on neural network. The corresponding controller design details are provided and the experiment results are given for verifying considerable advantages with our proposed control strategy in the end.