<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The work explores methods and develops aids to improve a pilot flight training level using Full Flight Simulators. It examines their didactic advantages over real helicopters by implementing on-board intelligent decision support systems (IDSS). These simulation programs monitor actions of learner, find the best ways to correct errors and generate appropriate instructions. The training is carried out by reference point methods while the helicopter flight dynamics model simulates a flight and helps an instructor in parallel. Sufficiently simple and accurate model of helicopter flight dynamics was selected and tested to assess the proposed methods and aids. Our analysis introduces a functional structure of IDSS as an adaptive control system with a reference model. We further conducted a study on developing models, methods and means of automatic analysis, forecasting, optimization and correction of actions. Combining single-criteria conditional and vector optimization methods, we found out optimal flight parameters. The research findings revealed that the chosen optimization method requires too many system resources for the optimal solution to be found in a short time. The study thereby comes up with applying ANNs to solve this problem.</p>
</abstract>

The work explores methods and develops aids to improve a pilot flight training level using Full Flight Simulators. It examines their didactic advantages over real helicopters by implementing on-board intelligent decision support systems (IDSS). These simulation programs monitor actions of learner, find the best ways to correct errors and generate appropriate instructions. The training is carried out by reference point methods while the helicopter flight dynamics model simulates a flight and helps an instructor in parallel. Sufficiently simple and accurate model of helicopter flight dynamics was selected and tested to assess the proposed methods and aids. Our analysis introduces a functional structure of IDSS as an adaptive control system with a reference model. We further conducted a study on developing models, methods and means of automatic analysis, forecasting, optimization and correction of actions. Combining single-criteria conditional and vector optimization methods, we found out optimal flight parameters. The research findings revealed that the chosen optimization method requires too many system resources for the optimal solution to be found in a short time. The study thereby comes up with applying ANNs to solve this problem.

An Approach to Developing Mathematical Software of On-Board Helicopter Flight Simulator Decision Support System

Kremenchuk Mykhailo Ostrohradskyi National University.

Department of Computer and Information Systems

Ivan Kozhedub Kharkiv National Air Force University

Department of Flight Operation and Combat Use of Helicopters

Department of Informatics and Natural Sciences

LOGI – Scientific Journal on Transport and Logistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The work explores methods and develops aids to improve a pilot flight training level using Full Flight Simulators. It examines their didactic advantages over...

An Approach to Developing Mathematical Software of On-Board Helicopter Flight Simulator Decision Support System

Online veröffentlicht: 28. Mai 2022

Seitenbereich: 61 - 72

Eingereicht: 14. Feb. 2022

Akzeptiert: 13. Apr. 2022

DOI: https://doi.org/10.2478/logi-2022-0006

Schlüsselwörter
Pilot training, flight simulators didactic features, on-board decision support system, mathematical modeling of pilot actions, automatic synthesis of instructions

© 2022 Mykola Guchenko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Approach to Developing Mathematical Software of On-Board Helicopter Flight Simulator Decision Support System

Online veröffentlicht: 28. Mai 2022

Seitenbereich: 61 - 72

Eingereicht: 14. Feb. 2022

Akzeptiert: 13. Apr. 2022

DOI: https://doi.org/10.2478/logi-2022-0006

SchlüsselwörterPilot training, flight simulators didactic features, on-board decision support system, mathematical modeling of pilot actions, automatic synthesis of instructions

© 2022 Mykola Guchenko et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Pilot training, flight simulators didactic features, on-board decision support system, mathematical modeling of pilot actions, automatic synthesis of instructions