Details of Grant 

EPSRC Reference:	EP/C014006/1
Title:	Stochastic model predictive control: theory and application to air-traffic control
Principal Investigator:	Maciejowski, Professor J
Other Investigators:	Lecchini Visintini, Dr A
Researcher Co-Investigators:
Project Partners:	Eurocontrol
Department:	Engineering
Organisation:	University of Cambridge
Scheme:	Standard Research (Pre-FEC)
Starts:	01 June 2005	Ends:	31 May 2009	Value (£):	275,004
EPSRC Research Topic Classifications:	Control Engineering
EPSRC Industrial Sector Classifications:	Aerospace, Defence and Marine
Related Grants:
Panel History:
Summary on Grant Application Form
The proposed work addresses the problem of controlling uncertain complex systems through the predictive control approach with particular application to Air-Traffic Control.The predictive approach to the control of uncertain dynamical systems consists of the following steps:1) Use a model to predict the evolution of the system in response to possible control actions;2) Use the resulting predictions to evaluate the best control action according to desired objectives;3) Apply the selected control action and collect information on the actual evolution of the system, then loop to Step 1.This strategy is very straightforward and has proved to be very effective in many applications. However, if the system is very complex and parts of it are not precisely known, a precise prediction model cannot be obtained. Prediction models that include some randomness in the evolution of the system can be used to simulate the effect uncertainties in the system. In these cases one has to make decisions in an uncertain scenario.The objective of the proposed work is to find an efficient strategy for selecting control actions in the face of uncertainty. The key idea will be to simulate a large number of possible evolutions of the system, in order to evaluate the average effect of each control action, and then choose on the basis of these evaluations. This idea, usually denoted as Monte Carlo approach, can be used to solve very complex problems and, in general, can be very straightforward to implement. However, its efficiency, in terms of speed and accuracy in finding the solution, can vary considerably depending on the particular strategy that has been followed to implement it. In this work, we will develop an efficient Monte Carlo strategy to the control of complex uncertain systems.The application to Air-Traffic Control has been selected because air-traffic is an uncertain complex system. The effect of the wind on aircraft trajectories and imprecise knowledge of the behaviour of pilots in response to Air-Traffic Control instructions are examples of the uncertainty that affect air-traffic. In the current system the strategy is that Air-Traffic Control issues very conservative instructions in order to remain well on the safe side. However increasing levels of traffic are pushing the current system towards its limits, so that there is a strong need for more sophisticated, innovative, Air-Traffic Control strategies.
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