Project: Robust Model Predictive Control

The figure on the right shows sets of velocity (Y-axis) vs position (X-axis). The blue region contains all the points from which an MPC planning problem can be solved. The purple region contains all the points that can be reached from the blue. Because the purple is inside the blue, the controller is robustly feasible - the state will always stay inside the blue. The controller being investigated here can be used for simple vehicle control problems. This result is part of on-going research on designing robust controllers.

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Project: Autonomous Vehicle Testbed

These robot vehicles are being developed for use in future projects. Rovers like these are used for testing and development of vehicle control algorithms. They have on-board computers, wireless network communications, and position sensing capability. They also have room for future upgrades including fitting on-board cameras or GPS for outdoor operations.

PhD Project: Cooperative Distributed Model Predictive Control

PhD Student Paul Trodden, supported by EPSRC and BAE Systems

Decentralized Model Predictive Control solves a separate planning problem for separate subsystems but ensures satisfaction of coupled constraints. Breaking up the problem results in a five-fold reduction in computation time, compared to solving a single planning problem for the team. In the figure on the right, eight aircraft use DMPC to solve a conflict situation and all reach their goals successfully without colliding. Thanks to a new cooperation strategy, no one aircraft takes a "greedy" route that would disadvantage all others.

PhD Project: Optimisation of Aircraft Taxi Operations

PhD Student Gillian Keith, supported by EPSRC and Airbus UK. This project is part of the Knowledge Transfer Network for Industrial Mathematics.

Future growth of air traffic demands faster air-to-gate-to-air transit at airports and the ICAO Advanced Surface Movement Guidance Control System specifically identifies the "routing" function as an opening for future autonomous operation. This project aims to apply advanced, robust, non-convex optimisation tools to the taxi routing problem, in order to reduce transit times, increase throughput capacity, and improve safety. For the latter, uncertainty will be considered explicitly, employing robust control and optimisation techniques. Furthermore, the project will investigate the computation, sensing and communication network infrastructure involved in this approach and will identify the functions and capabilities required on board each aircraft.

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PhD Project: Branch-and-Bound Optimisation for Collision Avoidance

Project by PhD Student Alison Eele, supported by EPSRC

This research involves a new approach to finding globally optimal solutions to avoidance problems, including UAVs avoiding radar threats and civil aircraft avoiding each other in free flight ATM. It should be faster than existing methods, because it can incorporate geometric knowledge in the optimisation algorithm instead of buying an expensive "black box" optimiser. It can also include more realistic, nonlinear dynamics models. Furthermore, we will research "hot start" methods for updating paths in the light of new information. Most existing work in this area simply restarts the optimisation from scratch.


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