Identification of Continuous-time Models from Sampled Data [electronic resource] / edited by Hugues Garnier, Liuping Wang.

By: Garnier, Hugues [editor.]Contributor(s): Wang, Liuping [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Advances in Industrial Control: Publisher: London : Springer London, 2008Description: XXVI, 413 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781848001619Subject(s): Engineering | Computer simulation | Statistics | Vibration | Engineering | Control Engineering | Simulation and Modeling | Vibration, Dynamical Systems, Control | Signal, Image and Speech Processing | Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences | Systems and Information Theory in EngineeringAdditional physical formats: Printed edition:: No titleOnline resources: Click here to access online
Contents:
Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance -- Estimation of Continuous-time Stochastic System Parameters -- Robust Identification of Continuous-time Systems from Sampled Data -- Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models -- Instrumental Variable Methods for Closed-loop Continuous-time Model Identification -- Model Order Identification for Continuous-time Models -- Estimation of the Parameters of Continuous-time Systems Using Data Compression -- Frequency-domain Approach to Continuous-time System Identification: Some Practical Aspects -- The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling -- Subspace-based Continuous-time Identification -- Process Parameter and Delay Estimation from Non-uniformly Sampled Data -- Iterative Methods for Identification of Multiple-input Continuous-time Systems with Unknown Time Delays -- Closed-loop Parametric Identification for Continuous-time Linear Systems via New Algebraic Techniques -- Continuous-time Model Identification Using Spectrum Analysis with Passivity-preserving Model Reduction.
In: Springer eBooksSummary: 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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Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance -- Estimation of Continuous-time Stochastic System Parameters -- Robust Identification of Continuous-time Systems from Sampled Data -- Refined Instrumental Variable Identification of Continuous-time Hybrid Box-Jenkins Models -- Instrumental Variable Methods for Closed-loop Continuous-time Model Identification -- Model Order Identification for Continuous-time Models -- Estimation of the Parameters of Continuous-time Systems Using Data Compression -- Frequency-domain Approach to Continuous-time System Identification: Some Practical Aspects -- The CONTSID Toolbox: A Software Support for Data-based Continuous-time Modelling -- Subspace-based Continuous-time Identification -- Process Parameter and Delay Estimation from Non-uniformly Sampled Data -- Iterative Methods for Identification of Multiple-input Continuous-time Systems with Unknown Time Delays -- Closed-loop Parametric Identification for Continuous-time Linear Systems via New Algebraic Techniques -- Continuous-time Model Identification Using Spectrum Analysis with Passivity-preserving Model Reduction.

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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