Application of preview information to pointing control of truss structure using artificial thermal expansion on orbit. (April 2015)

• Record Type: Journal Article
• Authors: Funakoshi, Yusuke; Ishimura, Kosei; Ogi, Yoshiro; Iwasa, Takashi
• Other Names: Dragoni Eugenio guest-editor.; Kiefer Bjoern guest-editor.; Koo Jeong-Hoi guest-editor.
• Is Part Of: Journal of intelligent material systems and structures. Volume 26:Number 6(2015:Apr.)
• Page Start: 730
• Page End: 739
• Publication Date: 2015-04
• Subjects: Pointing control -- large space telescope -- thermal expansion -- preview information -- model predictive control; Smart materials -- Periodicals; Intelligent control systems -- Periodicals; Artificial intelligence -- Periodicals; Matériaux intelligents -- Périodiques; Commande intelligente -- Périodiques; Intelligence artificielle -- Périodiques; 620.11
• DOI: 10.1177/1045389X14560360
• Languages: English
• ISSNs: 1045-389X

Abstract

The pointing performance of a truss structure on orbit that is used for a large space telescope is discussed. To achieve advanced science missions, large and precise support structures such as truss structures are needed. However, the preciseness of the structure might be lost due to various disturbances on orbit. Therefore, to realize ultra-large and precise support structures, active shape control of the structures is needed. To control the shape, we use artificial thermal expansion caused by heaters instead of mechanical actuators. Control systems without mechanical mechanisms have high reliability, which is very attractive for use on orbit. However, there are some constraints regarding the usage of heaters. The control input is restricted to positive inputs because heaters can give off heat but cannot dissipate heat actively, and there will be upper limits on the heat input. To improve the control performance under such constraints, we apply "model predictive control" as a feedforward control method with preview information. In this article, we mainly show the effectiveness of model predictive control compared with proportional–integral control, which is one of the typical feedback control methods. We developed a structural mathematical model and a thermal mathematical model in order to evaluate the performance of the control system. It is confirmed through numerical simulations that the total error is reduced by model predictive control compared with proportional–integral control.