System identification is an established field in the area of system
analysis and control. It aims to determine particular models for
dynamical systems based on observed inputs and outputs. Although
dynamical systems in the physical world are naturally described in the
continuous-time domain, most system identification schemes have been
based on discrete-time models without concern for the merits of natural
continuous-time model descriptions. The continuous-time nature of
physical laws, the persistent popularity of predominantly
continuous-time proportional-integral-derivative control and the more
direct nature of continuous-time fault diagnosis methods make
continuous-time modeling of ongoing importance.
Identification of Continuous-time Models from Sampled Data brings
together contributions from well-known experts who present an up-to-date
view of this active area of research and describe recent methods and
software tools developed in this field. They offer a fresh look at and
new results in areas such as:
- time and frequency domain optimal statistical approaches to
identification;
- parametric identification for linear, nonlinear and stochastic
systems;
- identification using instrumental variable, subspace and data
compression methods;
- closed-loop and robust identification; and
- continuous-time modeling from non-uniformly sampled data and for
systems with delay.
The CONtinuous-Time System IDentification (CONTSID) toolbox described in
the book gives an overview of developments and practical examples in
which MATLAB(R) can be brought to bear in the cause of direct
time-domain identification of continuous-time systems.This survey of
methods and results in continuous-time system identification will be a
valuable reference for a broad audience drawn from researchers and
graduate students in signal processing as well as in systems and
control. It also covers comprehensive material suitable for specialised
graduate courses in these areas.
