Theory and design of robust control systems
Project members: C.W. Scherer
Modern controller design techniques are based on models of
a physical plant which are obtained using first principles
or system identification. It is often possible to
incorporate predictable changes of the physical plant into
a model, such as variations in measurable parameters. In
addition, one might as well encounter unpredictable or
unmeasurable plant variations or system parts which are
hard if impossible to be approximated by simple models. In
order to cope with the latter effects, it is hence
reasonable to base the controller design on a whole set of
models which can be parameterized in a simple fashion.
Combining both uncertainty structures, any realistic
control design methodology should hence start with a
parameterized family of model sets, the parameter capturing
the measurable changes of the plant and the model sets
representing unpredictable system variations. The design
algorithm should lead to a parameterized or scheduled
family of controllers which achieves not only one but a
variety of design objectives for all elements in the model
set.
Performance objectives are either specified in a
qualitative manner (regulation, disturbance decoupling) or
they can be quantified using system norms or gains (with
the norm and norm as typical examples). For
linear time invariant systems, it is by now
wellestablished how to solve many of these problems
independently. However, the theory of designing controllers
achieving multiple objectives for one model or for a whole
model set, the socalled robust multiobjective design, is
still in its infancy.
The main goal of our research is to push further new
developments in the area of robust multiobjective control
and to combine the corresponding controller synthesis with
scheduling techniques if the system varies with a
measurable parameter.
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Up: Controller design
Previous: Controller design
