Using Machine Learning to train a Digital Test Pilot for missions in turbulent environments

Whilst piloted flight simulations in a full-motion simulator can be used to reduce costs by supporting the at-sea trials, the simulated trials are still expensive and are limited by simulator availability. The aim of this project is to develop a mathematical model of a human pilot, a Digital Test Pilot, which can be used to conduct multiple virtual deck landings in order to establish the likely boundaries of the safe operational envelope. The pilot will be 'trained' using the wealth of untapped data that is gathered by the test aircraft during the real-world sea trials. The ability to exploit this data could reap large cost-benefits and extract further capability in optimised design, maintenance and usage. The project will examine the suitability of methods for 'intelligent' use and fusion of available data to develop the Digital Test Pilot.

Desktop-based predictive simulation tools that use an objectively optimized human pilot modelling technique within an integrated pilot-vehicle-environment are available. A multi-loop pursuit pilot model, using a linearized helicopter flight dynamics model with a spatial air turbulence model, has shown potential for use as a predictive tool for operational clearances. In recent discussions with the UK MoD and Nova Systems, a more advanced modelling method has been proposed to use a non-linear helicopter flight model with an unrestricted turbulent air flow-field which the aircraft can 'explore' at will. The intention is to 'train' the Digital Test Pilot model using machine learning and data from the aircraft states and pilot control inputs derived from at-sea trails and from simulated flight trials undertaken within the University's own HELIFLIGHT-R simulator. The Digital Test Pilot model training will involve fusing data from a range of input sources e.g. aircraft state's, pilot control inputs, shipboard motion, to determine an appropriate response. Nova are prepared to financially support the project, and MoD have indicated they are prepared to provide real-world data; both are keen to provide expertise.

It has also been recognised that the process could be applied to non-military ship/helicopter operations as well as helicopters operating to offshore platforms and those in medical and rescue services. These avenues of further exploitation will also be examined in the project.

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.