Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation

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Allamaraju Subrahmanyam (Expedia Group, Greater Seattle Area, WA, USA) Ganti Prasada Rao

Identification of Continuous-Time Systems

Linear and Robust Parameter Estimation

Allamaraju Subrahmanyam (Expedia Group, Greater Seattle Area, WA, USA), Ganti Prasada Rao

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English

CRC Press
11 December 2019

Mathematics; Automatic control engineering

Series: Engineering Systems and Sustainability

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.

By: Allamaraju Subrahmanyam (Expedia Group Greater Seattle Area WA USA), Ganti Prasada Rao
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 750g
ISBN: 9780367371432
ISBN 10: 036737143X
Series: Engineering Systems and Sustainability
Pages: 120
Publication Date: 11 December 2019
Audience: College/higher education , General/trade , Primary , ELT Advanced
Format: Hardback
Publisher's Status: Active

1. Introduction and Overview 2. Markov Parameter Models 3. Time Moment Models 4. Robust Parameter Estimation 5. Error Quantification 6. Conclusions

Allamaraju Subrahmanyam, Ganti Prasada Rao