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

M. Guchenko, V. Shmakov, Anna Yudina, Vikroriya Belska, J. Čejka, L. Bartuška
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Abstract

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.
机载直升机飞行模拟器决策支持系统数学软件的开发方法
摘要:本文探讨了利用全飞行模拟器提高飞行员飞行训练水平的方法和辅助手段。它通过实施机载智能决策支持系统(IDSS)来检验它们相对于真实直升机的教学优势。这些模拟程序监控学习者的行为,找到最好的方法来纠正错误,并产生适当的指令。训练采用参考点法进行,同时直升机飞行动力学模型模拟飞行并并行帮助教官。选择了足够简单和准确的直升机飞行动力学模型并进行了测试,以评估所提出的方法和辅助工具。我们的分析介绍了IDSS作为一个参考模型的自适应控制系统的功能结构。进一步开展了动作自动分析、预测、优化和修正的模型、方法和手段的研究。结合单准则条件优化和矢量优化方法,找到了最优飞行参数。研究结果表明,所选择的优化方法需要过多的系统资源,难以在短时间内找到最优解。因此,本研究提出了应用人工神经网络来解决这一问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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