Ebook: Identification of Continuous-time Models from Sampled Data

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^® 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.

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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^® 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.

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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^® 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.

Content:
Front Matter....Pages i-xxvi
Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance....Pages 1-29
Estimation of Continuous-time Stochastic System Parameters....Pages 31-66
Robust Identification of Continuous-time Systems from Sampled Data....Pages 67-89
Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models....Pages 91-131
Instrumental Variable Methods for Closed-loop Continuous-time Model Identification....Pages 133-160
Model Order Identification for Continuous-time Models....Pages 161-187
Estimation of the Parameters of Continuous-time Systems Using Data Compression....Pages 189-213
Frequency-domain Approach to Continuous-time System Identification: Some Practical Aspects....Pages 215-248
The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling....Pages 249-290
Subspace-based Continuous-time Identification....Pages 291-311
Process Parameter and Delay Estimation from Non-uniformly Sampled Data....Pages 313-337
Iterative Methods for Identification of Multiple-input Continuous-time Systems with Unknown Time Delays....Pages 339-362
Closed-loop Parametric Identification for Continuous-time Linear Systems via New Algebraic Techniques....Pages 363-391
Continuous-time Model Identification Using Spectrum Analysis with Passivity-preserving Model Reduction....Pages 393-408
Back Matter....Pages 409-411

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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^® 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.

Content:
Front Matter....Pages i-xxvi
Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance....Pages 1-29
Estimation of Continuous-time Stochastic System Parameters....Pages 31-66
Robust Identification of Continuous-time Systems from Sampled Data....Pages 67-89
Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models....Pages 91-131
Instrumental Variable Methods for Closed-loop Continuous-time Model Identification....Pages 133-160
Model Order Identification for Continuous-time Models....Pages 161-187
Estimation of the Parameters of Continuous-time Systems Using Data Compression....Pages 189-213
Frequency-domain Approach to Continuous-time System Identification: Some Practical Aspects....Pages 215-248
The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling....Pages 249-290
Subspace-based Continuous-time Identification....Pages 291-311
Process Parameter and Delay Estimation from Non-uniformly Sampled Data....Pages 313-337
Iterative Methods for Identification of Multiple-input Continuous-time Systems with Unknown Time Delays....Pages 339-362
Closed-loop Parametric Identification for Continuous-time Linear Systems via New Algebraic Techniques....Pages 363-391
Continuous-time Model Identification Using Spectrum Analysis with Passivity-preserving Model Reduction....Pages 393-408
Back Matter....Pages 409-411
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