Download or read book State Estimation for Nonlinear Continuous Discrete Stochastic Systems written by Gennady Yu. Kulikov and published by Springer. This book was released on 2024-08-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the problem of accurate state estimation in nonlinear continuous-time stochastic models with additive noise and discrete measurements. Its main focus is on numerical aspects of computation of the expectation and covariance in Kalman-like filters rather than on statistical properties determining a model of the system state. Nevertheless, it provides the sound theoretical background and covers all contemporary state estimation techniques beginning at the celebrated Kalman filter, including its versions extended to nonlinear stochastic models, and till the most advanced universal Gaussian filters with deterministically sampled mean and covariance. In particular, the authors demonstrate that, when applying such filtering procedures to stochastic models with strong nonlinearities, the use of adaptive ordinary differential equation solvers with automatic local and global error control facilities allows the discretization error—and consequently the state estimation error—to be reduced considerably. For achieving that, the variable-stepsize methods with automatic error regulation and stepsize selection mechanisms are applied to treating moment differential equations arisen. The implemented discretization error reduction makes the self-adaptive nonlinear Gaussian filtering algorithms more suitable for application and leads to the novel notion of accurate state estimation. The book also discusses accurate state estimation in mathematical models with sparse measurements. Of special interest in this regard, it provides a means for treating stiff stochastic systems, which often encountered in applied science and engineering, being exemplified by the Van der Pol oscillator in electrical engineering and the Oregonator model of chemical kinetics. Square-root implementations of all Kalman-like filters considered and explored in this book for state estimation in Ill-conditioned continuous–discrete stochastic systems attract the authors’ particular attention. This book covers both theoretical and applied aspects of numerical integration methods, including the concepts of approximation, convergence, stiffness as well as of local and global errors, suitably for applied scientists and engineers. Such methods serve as a basis for the development of accurate continuous–discrete extended, unscented, cubature and many other Kalman filtering algorithms, including the universal Gaussian methods with deterministically sampled expectation and covariance as well as their mixed-type versions. The state estimation procedures in this book are presented in the fashion of complete pseudo-codes, which are ready for implementation and use in MATLAB® or in any other computation platform. These are examined numerically and shown to outperform traditional variants of the Kalman-like filters in practical prediction/filtering tasks, including state estimations of stiff and/or ill-conditioned continuous–discrete nonlinear stochastic systems.

Download or read book State Estimation of Nonlinear Continuous discrete Time Systems written by Hany Ismail El-Zorkany and published by 1971 [c1972]. This book was released on 1971 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal State Estimation written by Dan Simon and published by John Wiley & Sons. This book was released on 2006-06-19 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.

Download or read book Discrete time Stochastic Systems written by Torsten Söderström and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive introduction to the estimation and control of dynamic stochastic systems provides complete derivations of key results. The second edition includes improved and updated material, and a new presentation of polynomial control and new derivation of linear-quadratic-Gaussian control.

Download or read book Stochastic Systems and State Estimation written by Terrence P. McGarty and published by Wiley-Interscience. This book was released on 1974 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book State Estimation in Continuous Nonlinear Systems with Discrete Observations written by Glenn Marshall Sparks and published by . This book was released on 1970 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deterministic Sampling for Nonlinear Dynamic State Estimation written by Gilitschenski, Igor and published by KIT Scientific Publishing. This book was released on 2016-04-19 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.

Download or read book State Estimation for Dynamic Systems written by Felix L. Chernousko and published by CRC Press. This book was released on 1993-11-09 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: State Estimation for Dynamic Systems presents the state of the art in this field and discusses a new method of state estimation. The method makes it possible to obtain optimal two-sided ellipsoidal bounds for reachable sets of linear and nonlinear control systems with discrete and continuous time. The practical stability of dynamic systems subjected to disturbances can be analyzed, and two-sided estimates in optimal control and differential games can be obtained. The method described in the book also permits guaranteed state estimation (filtering) for dynamic systems in the presence of external disturbances and observation errors. Numerical algorithms for state estimation and optimal control, as well as a number of applications and examples, are presented. The book will be an excellent reference for researchers and engineers working in applied mathematics, control theory, and system analysis. It will also appeal to pure and applied mathematicians, control engineers, and computer programmers.

Download or read book State Estimation and Stabilization of Nonlinear Systems written by Abdellatif Ben Makhlouf and published by Springer Nature. This book was released on 2023-11-06 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the separation principle which is also known as the principle of separation of estimation and control and states that, under certain assumptions, the problem of designing an optimal feedback controller for a stochastic system can be solved by designing an optimal observer for the system's state, which feeds into an optimal deterministic controller for the system. Thus, the problem may be divided into two halves, which simplifies its design. In the context of deterministic linear systems, the first instance of this principle is that if a stable observer and stable state feedback are built for a linear time-invariant system (LTI system hereafter), then the combined observer and feedback are stable. The separation principle does not true for nonlinear systems in general. Another instance of the separation principle occurs in the context of linear stochastic systems, namely that an optimum state feedback controller intended to minimize a quadratic cost is optimal for the stochastic control problem with output measurements. The ideal solution consists of a Kalman filter and a linear-quadratic regulator when both process and observation noise are Gaussian. The term for this is linear-quadratic-Gaussian control. More generally, given acceptable conditions and when the noise is a martingale (with potential leaps), a separation principle, also known as the separation principle in stochastic control, applies when the noise is a martingale (with possible jumps).

Download or read book State Estimation for Nonlinear Systems Via Quasilinearization written by Wai Keung Chan and published by . This book was released on 1976 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book State Estimation of Nonlinear Discrete Time Systems microform written by N. Ramani and published by National Library of Canada. This book was released on 1972 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Filtering and Smoothing written by Simo Särkkä and published by Cambridge University Press. This book was released on 2013-09-05 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.

Download or read book Event Based State Estimation written by Dawei Shi and published by Springer. This book was released on 2015-11-19 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores event-based estimation problems. It shows how several stochastic approaches are developed to maintain estimation performance when sensors perform their updates at slower rates only when needed. The self-contained presentation makes this book suitable for readers with no more than a basic knowledge of probability analysis, matrix algebra and linear systems. The introduction and literature review provide information, while the main content deals with estimation problems from four distinct angles in a stochastic setting, using numerous illustrative examples and comparisons. The text elucidates both theoretical developments and their applications, and is rounded out by a review of open problems. This book is a valuable resource for researchers and students who wish to expand their knowledge and work in the area of event-triggered systems. At the same time, engineers and practitioners in industrial process control will benefit from the event-triggering technique that reduces communication costs and improves energy efficiency in wireless automation applications.

Download or read book Applied State Estimation and Association written by Chaw-Bing Chang and published by MIT Press. This book was released on 2023-08-15 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rigorous introduction to the theory and applications of state estimation and association, an important area in aerospace, electronics, and defense industries. Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications. Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.

Download or read book State Estimation of Nonlinear Discrete Time Systems written by Narayanaswami Ramani and published by . This book was released on 1970 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Moving Horizon State Estimation of Discrete Time Systems written by Peter Klaus Findeisen and published by . This book was released on 1997 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book State Estimation with Small Non linearities written by Björn Conrad and published by . This book was released on 1971 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: A variety of techniques are available for estimating the states of non-linear dynamic systems from noisy data. These procedures are generally equivalent when applied to linear systems. This report investigates the difference between several of these procedures in the presence of small dynamic and observational non-linearities. Four discrete estimation algorithms are analyzed. The first is a strictly least square estimator, while the other three are recursive algorithms similar to the Kalman filter used for estimating the states of linear systems. The product of this research is a group of analytic expressions for the mean and covariance of the error in each of those estimators so that they may be compared without lengthy Monte-Carlo simulations. The covariance expressions show that, to first order, all the estimators have the same covariance. Expressions for the means, however, show that each estimator has a different bias. Several examples are carried out demonstrating that the relative magnitudes of the bias errors in the various estimators can be a strong function of such parameters as initial covariances and number of data points being considered. In fact, under some circumstances it appears that more complicated (seemingly superior) algorithms can have larger biases than smaller ones. (Author).