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Design and flight testing actuator failure accommodation controllers on WVU YF-22 research UAVs.

机译:WVU YF-22研究型无人机的设计和飞行测试执行器故障适应控制器。

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摘要

This dissertation describes the design, development, and flight testing of a Neural Network (NN) based Fault Tolerant Flight Control System (FTFCS) with the ability to accommodate for actuator failures. The goal of this research was to demonstrate the ability of a specific set of control laws to maintain aircraft handling qualities in the presence of failures in the actuator channels. In this study, two-failure scenarios have been investigated: aileron failure (locking of the right aileron at a trim position) and elevator failure (locking of the right elevator at a trim position). A fleet of WVU YF-22 research aircraft test-beds were manufactured and instrumented for developing and testing of flight control software. An on-board payload with a PC-104 format computer system, sensors, and custom made circuit boards were designed and developed for these aircraft test-beds. The fault tolerant flight control systems for this study were designed to recover the aircraft with damaged actuators. On-board real-time data acquisition and control software was developed to achieve the Actuator Failure Accommodation (AFA) flight demonstration.; For the purposes of this research, control laws were required to be adaptive to changing aircraft dynamics during a failure scenario. On-line learning NNs---with their non-linearity and learning abilities---were used in the design of the on-board aircraft control scheme. The on-line training reduced the criticality of an extensive on-line Parameter IDentification (PID) during the failure and gives an on-board flight controller the capability to adjust to maintain the best possible flight performance during an unexpected failure.; This document will outline and describe the design and building of the flight controller, aircraft test-beds, on-board payload systems, and software in detail. Flight test results will be presented and documented to demonstrate the performance of a NN based FTFCS under failure conditions.
机译:本文介绍了一种能够适应执行器故障的基于神经网络的容错飞行控制系统(FTFCS)的设计,开发和飞行测试。这项研究的目的是证明在致动器通道出现故障的情况下,一套特定的控制法则能够保持飞机操纵质量的能力。在这项研究中,研究了两种故障情况:副翼故障(右副翼锁定在纵倾位置)和电梯故障(右升降机锁定在纵倾位置)。 WVU YF-22研究飞机测试平台的车队被制造出来并用于飞行控制软件的开发和测试。为这些飞机试验台设计并开发了带有PC-104格式计算机系统,传感器和定制电路板的机载有效载荷。本研究的容错飞行控制系统旨在恢复执行器损坏的飞机。开发了机载实时数据采集和控制软件,以实现执行器故障调节(AFA)飞行演示。出于本研究的目的,要求控制律必须适应故障场景中不断变化的飞机动力。在线学习神经网络-具有非线性和学习能力-被用于机载飞机控制方案的设计中。在线培训降低了故障期间广泛的在线参数IDentification(PID)的重要性,并使机载飞行控制器能够进行调整,以在意外故障期间保持最佳的飞行性能。本文档将概述并详细描述飞行控制器,飞机测试台,机载有效载荷系统和软件的设计和制造。飞行测试结果将被呈现并记录下来,以证明在故障条件下基于NN的FTFCS的性能。

著录项

  • 作者

    Gu, Yu.;

  • 作者单位

    West Virginia University.;

  • 授予单位 West Virginia University.;
  • 学科 Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 159 p.
  • 总页数 159
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 航空、航天技术的研究与探索;
  • 关键词

  • 入库时间 2022-08-17 11:43:12

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