Fuzzy logic application approach in control of automatic spacecraft

The increasing complexity and variety of tasks, the solution of which is placed on automatic systems, have recently determined an increased need for control systems with as much as possible universal properties. However, none of the currently existing approaches to building control systems — neither the theory of automatic control, nor artificial neural networks, nor other technologies, pos­sess the necessary universality. On the other hand, there is confidence in the possibility of constructing systems with the desired properties, which is based on fuzzy controllers. The main method of which is fuzzy logic and soft measure­ment. The use of soft measurement approaches implies the replacement of hard deter­ministic methods for determining the parameters and characteristics of a system or object by their tracking probability estimate. To obtain the most reliable re­sults, it is necessary to conduct long-term monitoring, taking into account the state of not only the object itself, but also the external environment. Fuzzy mod­eling provides effective methods and tools for studying systems in the event of insufficient or uncertainty of knowledge about the system under study, when ob­taining the required information is a difficult, time-consuming, expensive or im­possible task. Fuzzy neural networks or hybrid networks are designed to combine the ad­vantages of neural networks and fuzzy inference systems. On the one hand, they allow developing and presenting system models in the form of fuzzy production rules, which have clarity and simplicity of meaningful interpretation, on the oth­er hand, the capabilities of neural networks are used to construct fuzzy produc­tion rules. \ Such systems not only use a priori information, but can acquire new knowledge and are logically transparent to the user. This paper presents an overview possibility of fuzzy logic and soft measurement in control of automatic spacecraft. © 2020, Univelt Inc. All rights reserved.

Authors
Publisher
Univelt Inc.
Language
English
Pages
555-560
Status
Published
Volume
170
Year
2020
Organizations
  • 1 Academy of Engineering, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya str, Moscow, 117198, Russian Federation
  • 2 Innovative Oil and Gas Solutions Limited Liability company, 64/1 Shipilovskaya Street, Moscow, 115682, Russian Federation
Keywords
Automation; Computer circuits; Flight control systems; Fuzzy inference; Fuzzy neural networks; Object tracking; Space applications; Space flight; Space platforms; Building control systems; External environments; Fuzzy inference systems; Fuzzy logic applications; Fuzzy production rules; Theory of automatic control; Tracking probability; Universal properties; Fuzzy logic
Date of creation
02.11.2020
Date of change
05.02.2021
Short link
https://repository.rudn.ru/en/records/article/record/65306/
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