Sampled-data models for linear and nonlinear systems / Juan I. Yuz, Graham C. Goodwin

Includes bibliographical references and index

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

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