This thesis proposes a novel Model Predictive Control (MPC) strategy, which modifies the usual MPC cost function in order to achieve a desirable sparse actuation. It features an "1-regularised least squares loss function, in which the control error variance competes with the sum of input channels magnitude (or slew rate) over the whole horizon length. While standard control techniques lead to continuous movements of all actuators, this approach enables a selected subset of actuators to be used, the others being brought into play in exceptional circumstances. The same approach can also be used to obtain asynchronous actuator interventions, so that control actions are only taken in response to large disturbances. This thesis presents a straightforward and systematic approach to achieving these practical properties, which are ignored by mainstream control theory.

Marco Gallieri received a PhD inEngineering as an EPSRC scholar from Sidney Sussex College, the University ofCambridge, in 2014. His research was on Model Predictive Control forredundantly actuated systems, with focus on marine and air vehicles.  In 2007 he received a BSc and in 2009 an MScin information and industrial automation engineering from the Universita'Politecnica delle Marche, in Italy. He wrote his MSc thesis in 2009 during anErasmus exchange at the National University of Ireland Maynooth incollaboration with BioAtlantis Ltd and Enterprise Ireland. The topic wasmodeling and control design for a crane-vessel for seaweed harvesting.  Between May and September 2010 he was a MarieCurie early state researcher at the Instituto Superior Tecnico in Lisbon,working on non-linear methods for formation control of autonomous underwatervehicles with range only measurements. He is author of ten internationalconference papers as well as a Journal article.  Since February 2014 he is with McLaren Racing Ltd. From July2015 he is involved in the development of the F1 car simulator.Previously he worked as a control systems engineer and developed a model basedLi-Ion battery management system for the 2015 Honda power unit. Furtherrelevant projects included car speed and attitude estimation via sensor fusion,predictive analytics for fuel sensor management and fuel system designoptimization.