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

近年来，无人机的使用越来越多，从军事领域到民用领域都有广泛的应用。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很容易地找到应用实例。然而，与全尺寸直升机不同的是，在海上环境中使用无人直升机的例子寥寥无几，尽管快速部署、降低成本和任务灵活性的潜在好处是巨大的。这里的主要挑战是将无人直升机准确安全地降落在舰船甲板上，这需要在不利的海上环境中进行，如外部干扰、舰船运动和有限的作业空间。该项目旨在通过为在不利环境中运行的系统开发一个综合控制框架来应对这一挑战。它不仅依赖于传统的基于控制误差的反馈机制，而且能够预测环境对系统动态的影响并主动纠正这些影响。具体地说，通过融合基于干扰观测器的控制和模型预测控制两种强大的控制概念，并进一步扩展它们的功能，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。这一综合控制框架的开发将得到严格的理论分析的补充，并将通过在不利条件下的实际飞行试验进行验证。鉴于政府最近推动海上自主系统，拟议的研究使直升机能够在船舶甲板上自主着陆，将把无人驾驶直升机的优势带入海洋环境的广泛应用中。这将补充水面和海底海上航行器，形成真正的3-D海上自主能力。无人直升机可以以较低的成本更有效地执行环境监测、监视船只交通和移民流动以及货物供应等任务。允许它们在恶劣天气条件下作业将大大提高它们的可靠性，并降低海洋环境中的风险。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运送人道主义援助，以及从需要精确机动的海上化学品或石油泄漏中获取样本。此外，所提出的控制策略不仅可以用于其他类型的小型/微型无人机在恶劣条件下的控制综合，还可以用于其他应用领域，如自主水面车辆，其中干扰对系统动力学的影响也很大。

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.