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

近年来，无人机的应用日益广泛，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）由于其独特的悬停、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。示例应用可以很容易地在航空摄影，电影制作和基础设施检查中找到。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很少，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。这里的主要挑战是无人直升机在船舶甲板上准确安全地着陆，这需要在不利的海上环境中进行，例如外部干扰，船舶运动和有限的操作空间。该项目旨在通过开发在不利环境下运行的系统的综合控制框架来解决这一挑战。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力学的影响并主动纠正它们。具体而言，通过整合基于扰动观测器的控制和模型预测控制两个强大的控制概念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。这一综合控制框架的发展将得到严格的理论分析的补充，并通过不利条件下的实际飞行试验进行验证。鉴于最近政府对海上自主系统的推动，拟议的研究将使直升机在船舶甲板上自主着陆，这将使无人直升机的优势在海洋环境中得到广泛的应用。这将补充水面和水下航行器，形成真正的海上3d自主能力。无人驾驶直升机可以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，成本适中。允许它们在恶劣天气条件下运行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，这些都需要精确的操作。此外，设想所提出的控制策略不仅可以作为其他类型的小/微型无人机在不利条件下的控制综合工具，而且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.