Nonlinear Model Predictive Control of Thermal Vacuum Chamber Temperature

To ensure successful satellite operation, the space program conducts extensive ground tests on hardware. Among these tests, thermal test examines the performance and operation of units, subsystem, payloads, and entire satellites in thermal vacuum environments that satellites experience. Because few...

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Bibliographic Details
Published in:International journal of aeronautical and space sciences 2024, Vol.25 (1), p.213-228
Main Authors: Park, Sung-wook, Kim, Seungkeun
Format: Article
Language:English
Subjects:
Aerospace Technology and Astronautics
Engineering
Fluid- and Aerodynamics
Original Paper
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Summary:To ensure successful satellite operation, the space program conducts extensive ground tests on hardware. Among these tests, thermal test examines the performance and operation of units, subsystem, payloads, and entire satellites in thermal vacuum environments that satellites experience. Because few automation cases of thermal vacuum chamber temperature control exist, currently, temperature is manually controlled to frequently respond to irregular heat generation during thermal vacuum test. To overcome these limitations, this study aims to develop a thermal vacuum chamber temperature control algorithm. This study proposes a nonlinear model predictive control (NMPC) algorithm for unit-level thermal vacuum tests of a satellite. The NMPC algorithm is used to control the specimen temperature in a thermal vacuum chamber. The parameters of the system model equation are derived from system identification with real test data. The performance index of the NMPC and the correlation between the optimal performance index coefficient and the temperature change rate are presented. The performance of the proposed algorithm is verified through numerical simulation results for 20 cases. The proposed algorithm immediately responds to heat generation in the specimen during the functional test, and it well controls the specimen temperature within the tolerance range.
ISSN:2093-274X
2093-2480
DOI:10.1007/s42405-023-00639-8