Sampled-Data Models for Linear and Nonlinear Systems

In this book, rather than emphasize differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates as high as they are, it is more appropriate to emphasise connections and similarities.

Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives:

· the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable;

· although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and

· the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central.

The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either partially or completely erroneous. They also deal with linear and nonlinear, deterministic and stochastic cases. The impact of the ideas presented on several standard problems in signals and systems is illustrated using a number of applications.

Academic researchers and graduate students in systems, control and signal processing will find the ideas presented in Sampled-data Models for Linear and Nonlinear Systems to be a useful manual for dealing with sampled-data systems, clearing away mistaken ideas and bringing the subject thoroughly up to date. Researchers in statistics and economics will also derive benefit from the reworking of ideas relating a model derived from data sampling to an original continuous system.

Coverage of stochastic, deterministic, linear and nonlinear systems provides the reader with treatment of sampled-data systems of unparalleled breadth Clears up and corrects many common misconceptions about sampled-data systems Diverse applications demonstrate the real significance of the book's ideas within control and signal processing Includes supplementary material: sn.pub/extras

Klappentext
Sampled-data Models for Linear and Nonlinear Systems provides a fresh new look at a subject with which many researchers may think themselves familiar. Rather than emphasising the differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates being as high as they are, it is becoming more appropriate to emphasise connections and similarities. The text is driven by three motives:· the ubiquity of computers in modern control and signal-processing equipment means that sampling of systems that really evolve continuously is unavoidable;· although superficially straightforward, sampling can easily produce erroneous results when not treated properly; and· the need for a thorough understanding of many aspects of sampling among researchers and engineers dealing with applications to which they are central.The authors tackle many misconceptions which, although appearing reasonable at first sight, are in fact either partially or completely erroneous. They also deal with linear and nonlinear, deterministic and stochastic cases. The impact of the ideas presented on several standard problems in signals and systems is illustrated using a number of applications.Academic researchers and graduate students in systems, control and signal processing will find the ideas presented in Sampled-data Models for Linear and Nonlinear Systems to be a useful manual for dealing with sampled-data systems, clearing away mistaken ideas and bringing the subject thoroughly up to date. Researchers in statistics and economics will also derive benefit from the reworking of ideas relating a model derived from data sampling to an original continuous system. The Communications and Control Engineering series reports major technological advances which have potential for great impact in the fields of communication and control. It reflects research in industrial andacademic institutions around the world so that the readership can exploit new possibilities as they become available.

Inhalt
Part I: Deterministic Systems.- Background on Sampling of Signals.-Sampled Data Models for Linear Deterministic Systems.- Incremental Sampled Data Models.- Asymptotic Sampling Zeros.- Generalized Hold Devices.- Robustness.- Approximate Models for Linear Deterministic Systems.- Approximate Models for Deterministic Nonlinear Systems.- Applications of Approximate Sampled-data Models in Estimation and Control.- Part II: Stochastic Systems.- Background on Sampling of Stochastic Signals.- Sampled-data Models for Linear Stochastic Systems.- Incremental Stochastic Sampled-data Models.- Asymptotic Sampling Zeros for Linear Stochastic Systems.- Generalized Sampling Filters.- Approximate Sampled-data Models for Linear Stochastic Systems.- Stochastic Nonlinear Systems.- Approximate Sampled-data Models for Nonlinear Stochastic Systems.- Applications of Approximate Stochastic Sampled-data Models.- Part III: Embellishments and Extensions.- The Euler-Frobenius Polynominals.- Models for Intersample Response.- Approximate Sampled-data Models for Fractional Order Systems.