Download or read book Algorithms of Estimation for Nonlinear Systems written by Rafael Martínez-Guerra and published by Springer. This book was released on 2017-04-04 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book acquaints readers with recent developments in dynamical systems theory and its applications, with a strong focus on the control and estimation of nonlinear systems. Several algorithms are proposed and worked out for a set of model systems, in particular so-called input-affine or bilinear systems, which can serve to approximate a wide class of nonlinear control systems. These can either take the form of state space models or be represented by an input-output equation. The approach taken here further highlights the role of modern mathematical and conceptual tools, including differential algebraic theory, observer design for nonlinear systems and generalized canonical forms.
Download or read book Max Plus Methods for Nonlinear Control and Estimation written by William M. McEneaney and published by Springer Science & Business Media. This book was released on 2006-07-25 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: The central focus of this book is the control of continuous-time/continuous-space nonlinear systems. Using new techniques that employ the max-plus algebra, the author addresses several classes of nonlinear control problems, including nonlinear optimal control problems and nonlinear robust/H-infinity control and estimation problems. Several numerical techniques are employed, including a max-plus eigenvector approach and an approach that avoids the curse-of-dimensionality. The max-plus-based methods examined in this work belong to an entirely new class of numerical methods for the solution of nonlinear control problems and their associated Hamilton–Jacobi–Bellman (HJB) PDEs; these methods are not equivalent to either of the more commonly used finite element or characteristic approaches. Max-Plus Methods for Nonlinear Control and Estimation will be of interest to applied mathematicians, engineers, and graduate students interested in the control of nonlinear systems through the implementation of recently developed numerical methods.
Download or read book Least Squares Parameter Estimation Algorithms for Nonlinear Systems written by S. A. Billings and published by . This book was released on 1983 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book De wereld in perspectief written by and published by . This book was released on 1984 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Systems written by and published by BoD – Books on Demand. This book was released on 2018-07-18 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on several key aspects of nonlinear systems including dynamic modeling, state estimation, and stability analysis. It is intended to provide a wide range of readers in applied mathematics and various engineering disciplines an excellent survey of recent studies of nonlinear systems. With its thirteen chapters, the book brings together important contributions from renowned international researchers to provide an excellent survey of recent studies of nonlinear systems. The first section consists of eight chapters that focus on nonlinear dynamic modeling and analysis techniques, while the next section is composed of five chapters that center on state estimation methods and stability analysis for nonlinear systems.
Download or read book Nonlinear Filtering written by Kumar Pakki Bharani Chandra and published by Springer. This book was released on 2018-11-20 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives readers in-depth know-how on methods of state estimation for nonlinear control systems. It starts with an introduction to dynamic control systems and system states and a brief description of the Kalman filter. In the following chapters, various state estimation techniques for nonlinear systems are discussed, including the extended, unscented and cubature Kalman filters. The cubature Kalman filter and its variants are introduced in particular detail because of their efficiency and their ability to deal with systems with Gaussian and/or non-Gaussian noise. The book also discusses information-filter and square-root-filtering algorithms, useful for state estimation in some real-time control system design problems. A number of case studies are included in the book to illustrate the application of various nonlinear filtering algorithms. Nonlinear Filtering is written for academic and industrial researchers, engineers and research students who are interested in nonlinear control systems analysis and design. The chief features of the book include: dedicated coverage of recently developed nonlinear, Jacobian-free, filtering algorithms; examples illustrating the use of nonlinear filtering algorithms in real-world applications; detailed derivation and complete algorithms for nonlinear filtering methods, which help readers to a fundamental understanding and easier coding of those algorithms; and MATLAB® codes associated with case-study applications, which can be downloaded from the Springer Extra Materials website.
Download or read book Stable Adaptive Control and Estimation for Nonlinear Systems written by Jeffrey T. Spooner and published by John Wiley & Sons. This book was released on 2004-04-07 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thema dieses Buches ist die Anwendung neuronaler Netze und Fuzzy-Logic-Methoden zur Identifikation und Steuerung nichtlinear-dynamischer Systeme. Dabei werden fortgeschrittene Konzepte der herkömmlichen Steuerungstheorie mit den intuitiven Eigenschaften intelligenter Systeme kombiniert, um praxisrelevante Steuerungsaufgaben zu lösen. Die Autoren bieten viel Hintergrundmaterial; ausgearbeitete Beispiele und Übungsaufgaben helfen Studenten und Praktikern beim Vertiefen des Stoffes. Lösungen zu den Aufgaben sowie MATLAB-Codebeispiele sind ebenfalls enthalten.
Download or read book Parameter Estimation for Nonlinear Systems written by Leehter Yao and published by . This book was released on 1992 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonlinear Estimation written by Shovan Bhaumik and published by CRC Press. This book was released on 2019-07-24 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Estimation: Methods and Applications with Deterministic Sample Points focusses on a comprehensive treatment of deterministic sample point filters (also called Gaussian filters) and their variants for nonlinear estimation problems, for which no closed-form solution is available in general. Gaussian filters are becoming popular with the designers due to their ease of implementation and real time execution even on inexpensive or legacy hardware. The main purpose of the book is to educate the reader about a variety of available nonlinear estimation methods so that the reader can choose the right method for a real life problem, adapt or modify it where necessary and implement it. The book can also serve as a core graduate text for a course on state estimation. The book starts from the basic conceptual solution of a nonlinear estimation problem and provides an in depth coverage of (i) various Gaussian filters such as the unscented Kalman filter, cubature and quadrature based filters, Gauss-Hermite filter and their variants and (ii) Gaussian sum filter, in both discrete and continuous-discrete domain. Further, a brief description of filters for randomly delayed measurement and two case-studies are also included. Features: The book covers all the important Gaussian filters, including filters with randomly delayed measurements. Numerical simulation examples with detailed matlab code are provided for most algorithms so that beginners can verify their understanding. Two real world case studies are included: (i) underwater passive target tracking, (ii) ballistic target tracking. The style of writing is suitable for engineers and scientists. The material of the book is presented with the emphasis on key ideas, underlying assumptions, algorithms, and properties. The book combines rigorous mathematical treatment with matlab code, algorithm listings, flow charts and detailed case studies to deepen understanding.
Download or read book Nonlinear Systems written by Dongbin Lee and published by BoD – Books on Demand. This book was released on 2016-10-19 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book consists mainly of two parts: Chapter 1 - Chapter 7 and Chapter 8 - Chapter 14. Chapter 1 and Chapter 2 treat design techniques based on linearization of nonlinear systems. An analysis of nonlinear system over quantum mechanics is discussed in Chapter 3. Chapter 4 to Chapter 7 are estimation methods using Kalman filtering while solving nonlinear control systems using iterative approach. Optimal approaches are discussed in Chapter 8 with retarded control of nonlinear system in singular situation, and Chapter 9 extends optimal theory to H-infinity control for a nonlinear control system.Chapters 10 and 11 present the control of nonlinear dynamic systems, twin-rotor helicopter and 3D crane system, which are both underactuated, cascaded dynamic systems. Chapter 12 applies controls to antisynchronization/synchronization in the chaotic models based on Lyapunov exponent theorem, and Chapter 13 discusses developed stability analytic approaches in terms of Lyapunov stability. The analysis of economic activities, especially the relationship between stock return and economic growth, is presented in Chapter 14.
Download or read book Decomposition Algorithms for On line Estimation with Nonlinear Models written by Joao S. Albuquerque and published by . This book was released on 1994 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Dynamic state and parameter estimation for nonlinear systems usually leads to large nonlinear problems when the governing differential constraints are discretized under a simultaneous solution strategy. In this paper we discretize the set of ODEs using an Implicit Runge-Kutta integration method, and use the SQP method to solve the resulting NLP. The optimality conditions for each data set at the QP subproblem level are decoupled using an affine transform, so that the first order conditions in the state and input variables can be solved recursively and expressed as functions of the optimality conditions in the parameters, thus reducing the size of the problem and turning the effort of solving it linear with the number of data sets. As seen in our example, this approach is therefore over two orders of magnitude faster than general purpose NLP solvers."
Download or read book Nonlinear Estimation written by Gavin J.S. Ross and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer. A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier comparison of rival models. The book provides insights into why some models are difficult to fit, how to combine fits over different data sets, how to improve data collection to reduce prediction variance, and how to program particular models to handle a full range of data sets. The book combines an algebraic, a geometric and a computational approach, and is illustrated with practical examples. A final chapter shows how this approach is implemented in the author's Maximum Likelihood Program, MLP.
Download or read book Max Plus Methods for Nonlinear Control and Estimation written by William M. McEneaney and published by Springer Science & Business Media. This book was released on 2006 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The central focus of this book is the control of continuous-time/continuous-space nonlinear systems. Using new techniques that employ the max-plus algebra, the author addresses several classes of nonlinear control problems, including nonlinear optimal control problems and nonlinear robust/H-infinity control and estimation problems. Several numerical techniques are employed, including a max-plus eigenvector approach and an approach that avoids the curse-of-dimensionality. The max-plus-based methods examined in this work belong to an entirely new class of numerical methods for the solution of nonlinear control problems and their associated Hamilton–Jacobi–Bellman (HJB) PDEs; these methods are not equivalent to either of the more commonly used finite element or characteristic approaches. Max-Plus Methods for Nonlinear Control and Estimation will be of interest to applied mathematicians, engineers, and graduate students interested in the control of nonlinear systems through the implementation of recently developed numerical methods.
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 Nonlinear Estimation of Systems with and Without Delays written by David Feseng Liang and published by . This book was released on 1974 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, equations are derived for the state estimates and error-covariances of discrete and continuous nonlinear systems with and without delays, corrupted by white noise as well as non-white noise processes. In the case of continuous systems, new filtering algorithms are derived for nonlinear systems without delays, imbedded in white noise, correlated noise and noise free processes. The results obtained for white noise sequences are exact and optimal, with respect to the constraints imposed on the filtering dynamic equations, minimizing the error-variance cost functionals. The main technique makes use of the matrix minimum principle together with the Kolmogorov and Kushner equations to derive the optimal values of the coefficients in the estimation algorithms under the requirements that the estimates be unbiased. For non-white noise processes the results obtained are suboptimal since an approximate assumption has been made. The algorithms can be implemented in computer evaluation and are recursive in nature under the assumption that the conditional probability density functions of the estimator errors are Gaussian. Various nonlinear systems were simulated and compared with results obtained from some widely used finite dimensional approximate nonlinear filters. The results clearly indicate the superiority of the proposed minimum variance filter. Results pertaining to linear problems can be easily deduced from the nonlinear estimation algorithms, they agree well with those derived in the literature, using other optimization techniques. In the case of discrete-time systems, new nonlinear estimation algorithms, that directly yield the fixed-lag, fixed-point and fixed- interval smoothing and the filtering algorithms, are derived for nonlinear delayed systems with non-delayed measurements and multichannel time-delayed measurements, corrupted by white noise, correlated noise and colored noise processes. The derivation makes use of the concept of the gradient matrix to minimize the error-variance, taken to be the estimation criterion, under the condition that the estimates be unbiased. The derivation is straightforward and clearly indicate the close link between three different classification of smoothers and the filtering estimator. For systems with product types and polynomial nonlinearities, the only assumption needed to implement the algorithms in computer evaluation, is to assume that the conditional probability density functions of the estimator errors are Gaussian. They are expected to be computationally efficient, since no augmentation of state variables are introduced. And the results obtained are exact and optimal with respect to the imposed constraints on the dynamic equations of the estimators, minimizing the error variance cost functionals. The results can also be applied to various special cases of nonlinear as well as linear systems with and without delays. For linear systems, the results are identified with those in the literature.
Download or read book Modelling and Estimation Strategies for Fault Diagnosis of Non Linear Systems written by Marcin Witczak and published by Springer Science & Business Media. This book was released on 2007-04-05 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a variety of techniques that can be used for designing robust fault diagnosis schemes for non-linear systems. The introductory part of the book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. Subsequently, advanced robust observer structures are presented. Parameter estimation based techniques are discussed as well. A particular attention is drawn to experimental design for fault diagnosis. The book also presents a number of robust soft computing approaches utilizing evolutionary algorithms and neural networks. All approaches described in this book are illustrated by practical applications.
Download or read book Modelling and Estimation Strategies for Fault Diagnosis of Non Linear Systems written by Marcin Witczak and published by Springer. This book was released on 2009-09-02 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph presents a variety of techniques that can be used for designing robust fault diagnosis schemes for non-linear systems. The introductory part of the book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. Subsequently, advanced robust observer structures are presented. Parameter estimation based techniques are discussed as well. A particular attention is drawn to experimental design for fault diagnosis. The book also presents a number of robust soft computing approaches utilizing evolutionary algorithms and neural networks. All approaches described in this book are illustrated by practical applications.
