Download or read book Modern Control State Space Analysis and Design Methods written by Arie Nakhmani and published by McGraw Hill Professional. This book was released on 2020-05-01 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)
Download or read book Control System Design written by Bernard Friedland and published by Courier Corporation. This book was released on 2012-03-08 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book State Space Methods for Time Series Analysis written by Jose Casals and published by CRC Press. This book was released on 2018-09-03 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Linear State Space Control Systems written by Robert L. Williams, II and published by John Wiley & Sons. This book was released on 2007-02-09 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by Oxford University Press. This book was released on 2012-05-03 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.
Download or read book State Space Analysis of Control Systems written by Katsuhiko Ogata and published by . This book was released on 1967 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Fundamentals of Linear State Space Systems written by John S. Bay and published by McGraw-Hill Science, Engineering & Mathematics. This book was released on 1999 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Linear Control Theory written by Frederick Walker Fairman and published by John Wiley & Sons. This book was released on 1998-06-11 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Mathematical Systems Theory I written by Diederich Hinrichsen and published by Springer Science & Business Media. This book was released on 2011-08-03 with total page 818 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the mathematical foundations of systems theory in a self-contained, comprehensive, detailed and mathematically rigorous way. It is devoted to the analysis of dynamical systems and combines features of a detailed introductory textbook with that of a reference source. The book contains many examples and figures illustrating the text which help to bring out the intuitive ideas behind the mathematical constructions.
Download or read book An Introduction to State Space Time Series Analysis written by Jacques J. F. Commandeur and published by OUP Oxford. This book was released on 2007-07-19 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.
Download or read book State Space Analysis written by LaMar K. Timothy and published by . This book was released on 1968 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Problems after each chapter
Download or read book Practical Time Series Analysis written by Aileen Nielsen and published by O'Reilly Media. This book was released on 2019-09-20 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
Download or read book State Space Grids written by Tom Hollenstein and published by Springer Science & Business Media. This book was released on 2012-10-28 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human development from birth through adulthood is a complex interplay of many interacting forces. Children’s internal processes are manifest in behaviors that are sculpted by their experiences, most notably with primary caregivers. Because the discipline of psychology explores human behavior and cognition, the techniques employed for developmental analysis must be able to describe, depict, and quantify these complex processes. State Space Grids provides the framework, basic method, rationale, and advanced techniques for translating the behavior of children, adolescents, and parents into visible, traceable data. This seminar-between-covers takes readers step by step from conceptualization through implementation of projects, with examples from a range of current research within and outside child development. Links are included for the GridWare software program and related user resources. And although state space grids need not be used only to analyze dynamic systems, they serve as an excellent tool for honing systemic thinking. Key coverage in this volume includes: Dynamic systems and the origins of state space grids. The state of research using state space grids. Introducing GridWare and how it works. How to use state space grids, from idea through finished project. Within-grid and between-grid analysis. Conducting advanced analysis. State Space Grids is an essential reference for researchers across such disciplines as psychology, neuroscience, economics, computer science, and agricultural science.
Download or read book Advanced State Space Methods for Neural and Clinical Data written by Zhe Chen and published by Cambridge University Press. This book was released on 2015-10-15 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.
Download or read book State Space and Unobserved Component Models written by James Durbin and published by Cambridge University Press. This book was released on 2004-06-10 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.
Download or read book State Space Modeling of Time Series written by Masanao Aoki and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by Oxford University Press. This book was released on 2001-06-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
