This thesis presents both an applied study and a theoretical study within the
field of control theory. Control theory is an interdisciplinary branch between
mathematics and engineering dealing with the manipulation of systems to produce
a desired output.
The applied study deals with wind turbine control. Wind turbines are controlled
to optimize energy extraction from the wind. This must be done while respecting
physical restrictions and ensuring that loads on the wind turbine structure
does not seriously reduce the lifetime of components. This poses a trade-off
in the design and the wind turbine problem is hence a complex multivariable
problem. In this thesis the main focus is on design of controllers which optimally
attenuates the impact of the variability in the wind. The angles of the
wind turbine blades have been used as the primary control variable to achieve
this goal. Strategies have been studied in which the blades are controlled collectively
and individually. The wind has both temporal and spatial variations
with a stochastic nature. Furthermore, the wind has deterministic (or slowly
varying) trends. Large parts of the thesis hence deals with developing wind
models which can be used as disturbance models for controller design.
The theoretical study deals with Model Predictive Control (MPC). MPC is an
optimal control method which is characterized by the use of a receding prediction
horizon. MPC has risen in popularity due to its inherent ability to systematically
account for time-domain constraints on signals. During the last decades several
theoretical advances have been made, so that it can handle a wide variety of
system structures. In this thesis, the focus is on handling uncertain linear
system description. To this end the so-called Youla parameterizations have been
used. Two methods are proposed: The first method exploits the modularity of
the parameterizations so that the uncertainty can be identified and the MPC
controller can be reconfigured in a modular setting. The second method is a
robust MPC method in which the Youla parameters are used as an integral part
of the online optimization. In this way stability can be guaranteed given an
assumed bound on the uncertainty.
The contributions of the thesis have been documented in a series of scientific
papers. The papers form the main part of this thesis.