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Author: Arie Nakhmani Publisher: McGraw Hill Professional ISBN: 126045925X Category : Technology & Engineering Languages : en Pages : 192
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: Arie Nakhmani Publisher: McGraw Hill Professional ISBN: 126045925X Category : Technology & Engineering Languages : en Pages : 192
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: Bernard Friedland Publisher: Courier Corporation ISBN: 048613511X Category : Science Languages : en Pages : 530
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: Robert L. Williams, II Publisher: John Wiley & Sons ISBN: 0471735558 Category : Technology & Engineering Languages : en Pages : 485
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: John S. Bay Publisher: McGraw-Hill Science, Engineering & Mathematics ISBN: Category : Mathematics Languages : en Pages : 600
Book Description
Spans a broad range of linear system theory concepts, but does so in a complete and sequential style. It is suitable for a first-year graduate or advanced undergraduate course in any field of engineering. State space methods are derived from first principles while drawing on the students' previous understanding of physical and mathematical concepts. The text requires only a knowledge of basic signals and systems theory, but takes the student, in a single semester, all the way through state feedback, observers, Kalman filters, and elementary I.Q.G. control.
Author: Jose Casals Publisher: CRC Press ISBN: 131536025X Category : Mathematics Languages : en Pages : 286
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: James Durbin Publisher: OUP Oxford ISBN: 0191627194 Category : Business & Economics Languages : en Pages : 369
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: Junichiro Hagiwara Publisher: Springer Nature ISBN: 9811607117 Category : Mathematics Languages : en Pages : 350
Book Description
This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.
Author: James Durbin Publisher: Oxford University Press ISBN: 9780198523543 Category : Business & Economics Languages : en Pages : 280
Book Description
State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.
Author: Arie Nakhmani
Author: Arie Nakhmani
Author: LaMar K. Timothy
Author: Bernard Friedland
Author: Robert L. Williams, II
Author: John S. Bay
Author: Katsuhiko Ogata
Author: Jose Casals
Author: James Durbin
Author: Junichiro Hagiwara
Author: James Durbin