Adaptive Computing Technologies for Diagnostics and Control of UAVs With Fault-Tolerant On-Board Software
Abstract
The article deals with the issue of studying the principles and developing approaches to improving the fault tolerance of UAVs onboard software. The questions of analysis and study of the resource intensity of control and diagnostic algorithms, and the technology of balancing computational tasks using the methodology of distributed computing are formed. The use of hybrid modeling methods is appropriate in solving the problem, since in the work under the control of the UAV we will understand the purposeful impact on the functional processes that describe the implementation of the flight plan, route, selection and the mission as a whole. In this case, the established resource and time constraints must be taken into account. The scientific problem that the project is intended to solve is the creation of new adaptive distributed technologies for diagnosing and managing fault-tolerant on-board software for UAVs based on hybrid modeling methods. In the article, a methodology for modifying the algorithms for monitoring and diagnosing on-board software is formed, which is consistent with the multi-criteria characteristic of possible typical scenarios of UAV behavior.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Kovalev, I. V., Losev, V. V., Kovalev, D. I., Astanakulov, K. D., Voroshilova, A. A., Podoplelova, V. A., & Borovinsky, D. V. (2023). Adaptive Computing Technologies for Diagnostics and Control of UAVs With Fault-Tolerant On-Board Software. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 387-393). European Publisher. https://doi.org/10.15405/epct.23021.48