Many process control books focus on control design techniques,
taking the construction of a process model for granted. Process
Modelling for Control concentrates on the modelling steps
underlying a successful design, answering questions like:
How should I carry out the identification of my process in order
to obtain a good model?
How can I assess the quality of a model with a view to using it
in control design?
How can I ensure that a controller will stabilise a real process
sufficiently well before implementation?
What is the most efficient method of order reduction to
facilitate the implementation of high-order controllers?
Different tools, namely system identification, model/controller
validation and order reduction are studied in a framework with a
common basis: closed-loop identification with a controller that is
close to optimal will deliver models with bias and variance errors
ideally tuned for control design. As a result, rules are derived,
applying to all the methods, that provide the practitioner with a
clear way forward despite the apparently unconnected nature of the
modelling tools. Detailed worked examples, representative of
various industrial applications, are given: control of a
mechanically flexible structure; a chemical process; and a nuclear
power plant.
Process Modelling for Control uses mathematics of an
intermediate level convenient to researchers with an interest in
real applications and to practising control engineers interested in
control theory. It will enable working control engineers to improve
their methods and will provide academics and graduate students with
an all-round view of recent results in modelling for control.
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