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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Identification of Continuous-Time Systems - Linear and Robust Parameter Estimation (Hardcover)
Loot Price: R3,560
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Identification of Continuous-Time Systems - Linear and Robust Parameter Estimation (Hardcover)
Series: Engineering Systems and Sustainability
Expected to ship within 10 - 15 working days
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Models of dynamical systems are required for various purposes in
the field of systems and control. The models are handled either in
discrete time (DT) or in continuous time (CT). Physical systems
give rise to models only in CT because they are based on physical
laws which are invariably in CT. In system identification, indirect
methods provide DT models which are then converted into CT. Methods
of directly identifying CT models are preferred to the indirect
methods for various reasons. The direct methods involve a primary
stage of signal processing, followed by a secondary stage of
parameter estimation. In the primary stage, the measured signals
are processed by a general linear dynamic operation-computational
or realized through prefilters, to preserve the system parameters
in their native CT form-and the literature is rich on this aspect.
In this book: Identification of Continuous-Time Systems-Linear and
Robust Parameter Estimation, Allamaraju Subrahmanyam and Ganti
Prasada Rao consider CT system models that are linear in their
unknown parameters and propose robust methods of estimation. This
book complements the existing literature on the identification of
CT systems by enhancing the secondary stage through linear and
robust estimation. In this book, the authors provide an overview of
CT system identification, consider Markov-parameter models and
time-moment models as simple linear-in-parameters models for CT
system identification, bring them into mainstream model
parameterization via basis functions, present a methodology to
robustify the recursive least squares algorithm for parameter
estimation of linear regression models, suggest a simple off-line
error quantification scheme to show that it is possible to quantify
error even in the absence of informative priors, and indicate some
directions for further research. This modest volume is intended to
be a useful addition to the literature on identifying CT systems.
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