Autonomous landing of a helicopter at sea: advanced control in adverse conditions (AC2)

海上直升机自主着陆:不利条件下的先进控制(AC2)

基本信息

  • 批准号:
    EP/P012868/1
  • 负责人:
  • 金额:
    $ 12.86万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2017
  • 资助国家:
    英国
  • 起止时间:
    2017 至 无数据
  • 项目状态:
    已结题

项目摘要

The increasing use of unmanned aerial vehicles (UAVs) has spanned from the military domain to a wide range of civilian applications in recent years. Among many different types of UAVs, helicopters (or rotorcraft in general) have dominated in many applications because of their unique capabilities of hovering, low speed cruise and vertical take-off and landing (VTOL). Example applications can be easily found in aerial photography, film making and infrastructure inspection. However, unlike their full size counterparts, only few examples of using unmanned helicopters in maritime environments can be found, although the potential benefits of the rapid deployment, cost reduction and mission flexibility are great. The main challenge here is to land an unmanned helicopter accurately and safely on the deck of a ship, which needs to be conducted in an adverse maritime environment, such as external disturbances, ship movement and confined operational space. This project aims to tackle this challenge by developing an integrated control framework for systems operated in adverse environments. It not only relies on traditional feedback mechanisms based on control errors, but is also able to anticipate environmental influences on the system dynamics and rectify them proactively. Specifically, by consolidating two powerful control concepts (i.e. disturbance observer based control and model predictive control) and further expanding their capabilities, the developed control framework will be able to deal with the complicated helicopter dynamics and to take into account the external disturbances from different sources, so as to improve the control accuracy and robustness. The development of this integrated control framework will be complemented by rigorous theoretical analysis and validated by realistic flight tests under adverse conditions. In the light of the recent government promotion of maritime autonomous systems, the proposed research to enable autonomous landing of a helicopter on the deck of a ship would bring the advantages of unmanned helicopters into a vast range of applications in the maritime environment. This will complement surface and undersea maritime vehicles to form a truly 3-D autonomous capability at sea. Tasks such as environment monitoring, surveillance of vessel traffic and migrant flows, and cargo supply can be more efficiently performed by unmanned helicopters with modest cost. Allowing them to operate in adverse weather conditions will significantly improve their reliability and reduce the risks in the maritime environment. The proposed control framework will also play a critical role in fully exploring helicopters' VTOL capability in those tasks, for example to deliver humanitarian aid to boats with refugees and acquire samples from chemical or oil spills at sea, where precise manoeuvres are required. Moreover, it is envisaged that the proposed control strategy can be used as a control synthesis tool not only for other types of small/micro UAVs in adverse conditions, but also in other application domains like autonomous surface vehicles, where disturbance impacts on system dynamics are also significant.
近年来,无人机(UAV)的使用日益广泛,已从军事领域扩展到广泛的民用领域。在许多不同类型的无人机中,直升机(或一般的旋翼机)因其独特的悬停、低速巡航和垂直起降(VTOL)能力而在许多应用中占据主导地位。在航空摄影、电影制作和基础设施检查中可以轻松找到示例应用。然而,与全尺寸无人机不同的是,尽管快速部署、降低成本和任务灵活性的潜在好处很大,但在海上环境中使用无人直升机的例子却很少。这里的主要挑战是如何将无人直升机准确、安全地降落在船舶甲板上,这需要在恶劣的海上环境下进行,例如外部干扰、船舶运动和有限的操作空间。该项目旨在通过为在不利环境下运行的系统开发集成控制框架来应对这一挑战。它不仅依赖于基于控制误差的传统反馈机制,而且还能够预测环境对系统动态的影响并主动纠正。具体来说,通过整合两个强大的控制概念(即基于扰动观测器的控制和模型预测控制)并进一步扩展其功能,所开发的控制框架将能够处理复杂的直升机动力学并考虑来自不同来源的外部扰动,从而提高控制精度和鲁棒性。这种综合控制框架的开发将得到严格的理论分析的补充,并通过恶劣条件下的实际飞行测试进行验证。鉴于最近政府推广海上自主系统,拟议的研究使直升机能够在船舶甲板上自主着陆,这将把无人直升机的优势带入海上环境的广泛应用。这将补充水面和水下海上车辆,形成真正的海上3D自主能力。无人直升机可以以较低的成本更有效地执行环境监测、船舶交通和移民流动监视以及货物供应等任务。允许它们在恶劣的天气条件下运行将显着提高其可靠性并降低海上环境的风险。拟议的控制框架还将在充分探索直升机在这些任务中的垂直起降能力方面发挥关键作用,例如向载有难民的船只提供人道主义援助以及从海上化学品或石油泄漏中获取样本,这些都需要精确的机动。此外,预计所提出的控制策略不仅可以用作不利条件下其他类型的小型/微型无人机的控制综合工具,而且还可以用于其他应用领域,例如自主地面车辆,其中扰动对系统动力学的影响也很显着。

项目成果

期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Optimal Path Following for Small Fixed-Wing UAVs Under Wind Disturbances
  • DOI:
    10.1109/tcst.2020.2980727
  • 发表时间:
    2020-04
  • 期刊:
  • 影响因子:
    4.8
  • 作者:
    Jun Yang;Cunjia Liu;M. Coombes;Yunda Yan;Wen‐Hua Chen
  • 通讯作者:
    Jun Yang;Cunjia Liu;M. Coombes;Yunda Yan;Wen‐Hua Chen
Surviving disturbances: A predictive control framework with guaranteed safety
  • DOI:
    10.1016/j.automatica.2023.111238
  • 发表时间:
    2023-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yunda Yan;Xue‐Fang Wang;Ben Marshall;Cunjia Liu;Jun Yang;Wen-Hua Chen
  • 通讯作者:
    Yunda Yan;Xue‐Fang Wang;Ben Marshall;Cunjia Liu;Jun Yang;Wen-Hua Chen
Actuator Dynamics Augmented DOBC for A Small Fixed Wing UAV
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jean Smith;Jun Yang;Wen‐Hua Chen;J. Yang;C. Liu
  • 通讯作者:
    Jean Smith;Jun Yang;Wen‐Hua Chen;J. Yang;C. Liu
A Simple Optimal Planer Path Following Algorithm for Unmanned Aerial Vehicles∗
一种简单的无人机最优平面路径跟随算法*
  • DOI:
    10.23919/ecc.2018.8550125
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jun Yang;Cunjia Liu;Zongyu Zuo;Wen‐Hua Chen
  • 通讯作者:
    Wen‐Hua Chen
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Cunjia Liu其他文献

Adaptive informative path planning for active reconstruction of spatio-temporal water pollution dispersion using Unmanned Surface Vehicles
利用无人水面艇进行时空水污染扩散主动重建的自适应信息路径规划
  • DOI:
    10.1016/j.apor.2025.104458
  • 发表时间:
    2025-03-01
  • 期刊:
  • 影响因子:
    4.400
  • 作者:
    Song Ma;Cunjia Liu;Christopher M. Harvey;Richard Bucknall;Yuanchang Liu
  • 通讯作者:
    Yuanchang Liu
Dual Control Inspired Active Sensing for Bearing-Only Target Tracking
双控制启发的主动传感,用于仅方位目标跟踪
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Timothy J. Glover;Cunjia Liu;Wen
  • 通讯作者:
    Wen
Disturbance Rejection for Nonlinear Uncertain Systems With Output Measurement Errors: Application to a Helicopter Model
具有输出测量误差的非线性不确定系统的抗扰:在直升机模型中的应用
On the Actuator Dynamics of Dynamic Control Allocation for a Small Fixed-Wing UAV With Direct Lift Control
直接升力控制小型固定翼无人机动态控制分配的作动器动力学研究
Fruit tree canopy segmentation from UAV orthophoto maps based on a lightweight improved U-Net
基于轻量级改进U-Net的无人机正射影像果树冠层分割
  • DOI:
    10.1016/j.compag.2023.108538
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Zhikai Li;Xiaoling Deng;Yubin Lan;Cunjia Liu;Jiajun Qing
  • 通讯作者:
    Jiajun Qing

Cunjia Liu的其他文献

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{{ truncateString('Cunjia Liu', 18)}}的其他基金

Space-enabled Crop disEase maNagement sErvice via Crop sprAying Drones (SCENE-CAD)
通过作物喷洒无人机提供太空作物病害管理服务 (SCENE-CAD)
  • 批准号:
    ST/V00137X/1
  • 财政年份:
    2020
  • 资助金额:
    $ 12.86万
  • 项目类别:
    Research Grant
Persistence through Reliable Perching (PEP)
通过可靠栖息 (PEP) 实现持久性
  • 批准号:
    EP/R005494/1
  • 财政年份:
    2017
  • 资助金额:
    $ 12.86万
  • 项目类别:
    Research Grant

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