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.
