Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods
Apstrakt
Artificial intelligence (AI) promises breakthroughs in space operations, from mission design planning to satellite data processing and navigation systems. Advances in AI and space transportation have enabled AI technologies in spacecraft tracking control and synchronization. This study assesses and evaluates three alternative spacecraft tracking control and synchronization (TCS) approaches, including non-AI TCS methods, AI TCS methods, and combined TCS methods. The study proposes a hybrid model, including a new model for defining weight coefficients and interval type-2 fuzzy sets based combined compromised solution (IT2FSs-CoCoSo) to solve the spacecraft TCS problem. A new methodology is used to calculate the weight coefficients of criteria, while IT2FSs-CoCoSo is applied to rank the prioritization of TCS methods. A comparative analysis is conducted to demonstrate the performance of the proposed hybrid model. We present a case study to illustrate the applicability and exhibit the effic...acy of the proposed method for prioritizing the alternative TCS approaches based on ten different sub-criteria, grouped under three main aspects, including complexity aspects, operational aspects, and efficiency aspects. AI and non-AI methods combined are the most advantageous alternative, whereas non-AI methods are the least advantageous, according to the findings of this study.
Ključne reči:
Tracking control / Synchronization of spacecraft / Multi-criteria decision making / Interval type-2 fuzzy sets / CoCoSo / Artificial intelligenceIzvor:
Advances in Space Research, 2023, 71, 9, 3534-3551Izdavač:
- Elsevier Ltd
Institucija/grupa
Fakultet organizacionih naukaTY - JOUR AU - Deveci, Muhammet AU - Pamučar, Dragan AU - Gokasar, Ilgin AU - Tavana, M. PY - 2023 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2518 AB - Artificial intelligence (AI) promises breakthroughs in space operations, from mission design planning to satellite data processing and navigation systems. Advances in AI and space transportation have enabled AI technologies in spacecraft tracking control and synchronization. This study assesses and evaluates three alternative spacecraft tracking control and synchronization (TCS) approaches, including non-AI TCS methods, AI TCS methods, and combined TCS methods. The study proposes a hybrid model, including a new model for defining weight coefficients and interval type-2 fuzzy sets based combined compromised solution (IT2FSs-CoCoSo) to solve the spacecraft TCS problem. A new methodology is used to calculate the weight coefficients of criteria, while IT2FSs-CoCoSo is applied to rank the prioritization of TCS methods. A comparative analysis is conducted to demonstrate the performance of the proposed hybrid model. We present a case study to illustrate the applicability and exhibit the efficacy of the proposed method for prioritizing the alternative TCS approaches based on ten different sub-criteria, grouped under three main aspects, including complexity aspects, operational aspects, and efficiency aspects. AI and non-AI methods combined are the most advantageous alternative, whereas non-AI methods are the least advantageous, according to the findings of this study. PB - Elsevier Ltd T2 - Advances in Space Research T1 - Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods EP - 3551 IS - 9 SP - 3534 VL - 71 DO - 10.1016/j.asr.2022.07.056 UR - conv_3731 ER -
@article{ author = "Deveci, Muhammet and Pamučar, Dragan and Gokasar, Ilgin and Tavana, M.", year = "2023", abstract = "Artificial intelligence (AI) promises breakthroughs in space operations, from mission design planning to satellite data processing and navigation systems. Advances in AI and space transportation have enabled AI technologies in spacecraft tracking control and synchronization. This study assesses and evaluates three alternative spacecraft tracking control and synchronization (TCS) approaches, including non-AI TCS methods, AI TCS methods, and combined TCS methods. The study proposes a hybrid model, including a new model for defining weight coefficients and interval type-2 fuzzy sets based combined compromised solution (IT2FSs-CoCoSo) to solve the spacecraft TCS problem. A new methodology is used to calculate the weight coefficients of criteria, while IT2FSs-CoCoSo is applied to rank the prioritization of TCS methods. A comparative analysis is conducted to demonstrate the performance of the proposed hybrid model. We present a case study to illustrate the applicability and exhibit the efficacy of the proposed method for prioritizing the alternative TCS approaches based on ten different sub-criteria, grouped under three main aspects, including complexity aspects, operational aspects, and efficiency aspects. AI and non-AI methods combined are the most advantageous alternative, whereas non-AI methods are the least advantageous, according to the findings of this study.", publisher = "Elsevier Ltd", journal = "Advances in Space Research", title = "Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods", pages = "3551-3534", number = "9", volume = "71", doi = "10.1016/j.asr.2022.07.056", url = "conv_3731" }
Deveci, M., Pamučar, D., Gokasar, I.,& Tavana, M.. (2023). Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods. in Advances in Space Research Elsevier Ltd., 71(9), 3534-3551. https://doi.org/10.1016/j.asr.2022.07.056 conv_3731
Deveci M, Pamučar D, Gokasar I, Tavana M. Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods. in Advances in Space Research. 2023;71(9):3534-3551. doi:10.1016/j.asr.2022.07.056 conv_3731 .
Deveci, Muhammet, Pamučar, Dragan, Gokasar, Ilgin, Tavana, M., "Spacecraft tracking control and synchronization: An assessment of conventional, unconventional, and combined methods" in Advances in Space Research, 71, no. 9 (2023):3534-3551, https://doi.org/10.1016/j.asr.2022.07.056 ., conv_3731 .