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Author: Jose Casals
Publisher: CRC Press
ISBN: 131536025X
Category : Mathematics
Languages : en
Pages : 286
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Book Description
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.
Author: Jose Casals
Publisher: CRC Press
ISBN: 131536025X
Category : Mathematics
Languages : en
Pages : 286
Get Book
Book Description
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.
Author: Bernard Friedland
Publisher: Courier Corporation
ISBN: 048613511X
Category : Science
Languages : en
Pages : 530
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Book Description
Introduction to state-space methods covers feedback control; state-space representation of dynamic systems and dynamics of linear systems; frequency-domain analysis; controllability and observability; shaping the dynamic response; more. 1986 edition.
Author: Frank M. Callier
Publisher: Springer Science & Business Media
ISBN: 1461209579
Category : Technology & Engineering
Languages : en
Pages : 524
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Book Description
This book is the result of our teaching over the years an undergraduate course on Linear Optimal Systems to applied mathematicians and a first-year graduate course on Linear Systems to engineers. The contents of the book bear the strong influence of the great advances in the field and of its enormous literature. However, we made no attempt to have a complete coverage. Our motivation was to write a book on linear systems that covers finite dimensional linear systems, always keeping in mind the main purpose of engineering and applied science, which is to analyze, design, and improve the performance of phy sical systems. Hence we discuss the effect of small nonlinearities, and of perturbations of feedback. It is our on the data; we face robustness issues and discuss the properties hope that the book will be a useful reference for a first-year graduate student. We assume that a typical reader with an engineering background will have gone through the conventional undergraduate single-input single-output linear systems course; an elementary course in control is not indispensable but may be useful for motivation. For readers from a mathematical curriculum we require only familiarity with techniques of linear algebra and of ordinary differential equations.
Author: Robert L. Williams, II
Publisher: John Wiley & Sons
ISBN: 0471735558
Category : Technology & Engineering
Languages : en
Pages : 485
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Book Description
The book blends readability and accessibility common to undergraduate control systems texts with the mathematical rigor necessary to form a solid theoretical foundation. Appendices cover linear algebra and provide a Matlab overivew and files. The reviewers pointed out that this is an ambitious project but one that will pay off because of the lack of good up-to-date textbooks in the area.
Author: James Durbin
Publisher: OUP Oxford
ISBN: 0191627194
Category : Business & Economics
Languages : en
Pages : 369
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Book Description
This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.
Author: Nicolae Lobontiu
Publisher: Academic Press
ISBN: 0124172091
Category : Technology & Engineering
Languages : en
Pages : 786
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Book Description
Engineering system dynamics focuses on deriving mathematical models based on simplified physical representations of actual systems, such as mechanical, electrical, fluid, or thermal, and on solving these models for analysis or design purposes. System Dynamics for Engineering Students: Concepts and Applications features a classical approach to system dynamics and is designed to be utilized as a one-semester system dynamics text for upper-level undergraduate students with emphasis on mechanical, aerospace, or electrical engineering. It is the first system dynamics textbook to include examples from compliant (flexible) mechanisms and micro/nano electromechanical systems (MEMS/NEMS). This new second edition has been updated to provide more balance between analytical and computational approaches; introduces additional in-text coverage of Controls; and includes numerous fully solved examples and exercises. Features a more balanced treatment of mechanical, electrical, fluid, and thermal systems than other texts Introduces examples from compliant (flexible) mechanisms and MEMS/NEMS Includes a chapter on coupled-field systems Incorporates MATLAB® and Simulink® computational software tools throughout the book Supplements the text with extensive instructor support available online: instructor's solution manual, image bank, and PowerPoint lecture slides NEW FOR THE SECOND EDITION Provides more balance between analytical and computational approaches, including integration of Lagrangian equations as another modelling technique of dynamic systems Includes additional in-text coverage of Controls, to meet the needs of schools that cover both controls and system dynamics in the course Features a broader range of applications, including additional applications in pneumatic and hydraulic systems, and new applications in aerospace, automotive, and bioengineering systems, making the book even more appealing to mechanical engineers Updates include new and revised examples and end-of-chapter exercises with a wider variety of engineering applications
Author: Daniel Alpay
Publisher: Springer Science & Business Media
ISBN: 9783764373702
Category : Language Arts & Disciplines
Languages : en
Pages : 292
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Book Description
The state space method developed in the last decades allows us to study the theory of linear systems by using tools from the theory of linear operators; conversely, it had a strong influence on operator theory introducing new questions and topics. The present volume contains a collection of essays representing some of the recent advances in the state space method. Methods covered include noncommutative systems theory, new aspects of the theory of discrete systems, discrete analogs of canonical systems, and new applications to the theory of Bezoutiants and convolution equations. The articles in the volume will be of interest to pure and applied mathematicians, electrical engineers and theoretical physicists.
Author: Arie Nakhmani
Publisher: McGraw Hill Professional
ISBN: 126045925X
Category : Technology & Engineering
Languages : en
Pages : 192
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Book Description
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Apply a state-space approach to modern control system analysis and design Written by an expert in the field, this concise textbook offers hands-on coverage of modern control system engineering. Modern Control: State-Space Analysis and Design Methods features start-to-finish design projects as well as online snippets of MATLAB code with simulations. The essential mathematics are presented along with fully worked-out examples in gradually increasing degrees of difficulty. Readers will receive “just-in-time” math background from a comprehensive appendix and get step-by-step descriptions of the latest analysis and design techniques. Coverage includes: • An introduction to control systems • State-space representations • Pole placement via state feedback • State estimators (observers) • Non-minimal canonical forms • Linearization • Lyapunov stability • Linear quadratic regulators (LQR) • Symmetric root locus (SRL) • Kalman filter • Linear quadratic gaussian control (LQG)
Author: Frederick Walker Fairman
Publisher: John Wiley & Sons
ISBN: 9780471974895
Category : Science
Languages : en
Pages : 338
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Book Description
Incorporating recent developments in control and systems research,Linear Control Theory provides the fundamental theoreticalbackground needed to fully exploit control system design software.This logically-structured text opens with a detailed treatment ofthe relevant aspects of the state space analysis of linear systems.End-of-chapter problems facilitate the learning process byencouraging the student to put his or her skills into practice.Features include: * The use of an easy to understand matrix variational technique todevelop the time-invariant quadratic and LQG controllers * A step-by-step introduction to essential mathematical ideas asthey are needed, motivating the reader to venture beyond basicconcepts * The examination of linear system theory as it relates to controltheory * The use of the PBH test to characterize eigenvalues in the statefeedback and observer problems rather than its usual role as a testfor controllability or observability * The development of model reduction via balanced realization * The employment of the L2 gain as a basis for the development ofthe H??? controller for the design of controllers in the presenceof plant model uncertainty Senior undergraduate and postgraduate control engineering studentsand practicing control engineers will appreciate the insight thisself-contained book offers into the intelligent use of today scontrol system software tools.
Author: Masanao Aoki
Publisher: Springer Science & Business Media
ISBN: 3642969852
Category : Business & Economics
Languages : en
Pages : 324
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Book Description
model's predictive capability? These are some of the questions that need to be answered in proposing any time series model construction method. This book addresses these questions in Part II. Briefly, the covariance matrices between past data and future realizations of time series are used to build a matrix called the Hankel matrix. Information needed for constructing models is extracted from the Hankel matrix. For example, its numerically determined rank will be the di mension of the state model. Thus the model dimension is determined by the data, after balancing several sources of error for such model construction. The covariance matrix of the model forecasting error vector is determined by solving a certain matrix Riccati equation. This matrix is also the covariance matrix of the innovation process which drives the model in generating model forecasts. In these model construction steps, a particular model representation, here referred to as balanced, is used extensively. This mode of model representation facilitates error analysis, such as assessing the error of using a lower dimensional model than that indicated by the rank of the Hankel matrix. The well-known Akaike's canonical correlation method for model construc tion is similar to the one used in this book. There are some important differ ences, however. Akaike uses the normalized Hankel matrix to extract canonical vectors, while the method used in this book does not normalize the Hankel ma trix.
