Download or read book Neural Network Based State Estimation of Nonlinear Systems written by Heidar A. Talebi and published by Springer. This book was released on 2009-12-04 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.
Download or read book Neural Network Based State Estimation of Nonlinear Systems written by Heidar A. Talebi and published by Springer. This book was released on 2009-12-14 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.
Download or read book Neural Network Based Adaptive Control of Uncertain Nonlinear Systems written by Kasra Esfandiari and published by Springer Nature. This book was released on 2021-06-18 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.
Download or read book Differential Neural Networks for Robust Nonlinear Control written by Alexander S. Poznyak and published by World Scientific. This book was released on 2001 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.
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 Identification of Nonlinear Systems Using Neural Networks and Polynomial Models written by Andrzej Janczak and published by Springer Science & Business Media. This book was released on 2004-11-18 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph systematically presents the existing identification methods of nonlinear systems using the block-oriented approach It surveys various known approaches to the identification of Wiener and Hammerstein systems which are applicable to both neural network and polynomial models. The book gives a comparative study of their gradient approximation accuracy, computational complexity, and convergence rates and furthermore presents some new and original methods concerning the model parameter adjusting with gradient-based techniques. "Identification of Nonlinear Systems Using Neural Networks and Polynomal Models" is useful for researchers, engineers and graduate students in nonlinear systems and neural network theory.
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 Exploration of the Use of Deep Neural Networks for Joint Parameter and State Estimation of Linear and Nonlinear Systems written by Huiyuan Yang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The deep neural network has demonstrated exceptional performance in many engineering disciplines. In this thesis, We compare the state and parameter estimation performance between the deep neural network and the Reproducing Kernel Hilbert Space (RKHS). we utilize the feedforward neural network model to estimate the state and parameter of a third order linear time invariant system and two nonlinear dynamic systems: Sedoglavic equation and Van der Pol equation. The results indicate that the deep neural network shows comparable performance in recovering the true state and parameter from various levels of noise data with the state-of-the-art RKHS method on the third order linear time invariant system. We also demonstrate the capability of the deep neural network on parameter and state estimation of the single and multi-parameter nonlinear dynamic systems"--
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 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 Parameter Estimation and Adaptive Control for Nonlinear Servo Systems written by Shubo Wang and published by Elsevier. This book was released on 2024-01-16 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter Estimation and Adaptive Control for Nonlinear Servo Systems presents the latest advances in observer-based control design, focusing on adaptive control for nonlinear systems such as adaptive neural network control, adaptive parameter estimation, and system identification. This book offers an array of new real-world applications in the field. Written by eminent scientists in the field of control theory, this book covers the latest advances in observer-based control design. It provides fundamentals, algorithms, and it discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems. - Presents a clear and concise introduction to the latest advances in parameter estimation and adaptive control with several concise applications for servo systems - Covers a wide range of applications usually not found in similar books, such as power systems, robotics, mechatronics, aeronautics, and industrial systems - Contains worked examples which make it ideal for advanced courses as well as for researchers starting to work in the field, particularly suitable for engineers wishing to enter the field quickly and efficiently
Download or read book Artificial Higher Order Neural Networks for Modeling and Simulation written by Zhang, Ming and published by IGI Global. This book was released on 2012-10-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.
Download or read book Network and Communication Technology Innovations for Web and IT Advancement written by Alkhatib, Ghazi I. and published by IGI Global. This book was released on 2012-10-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the steady stream of new web based information technologies being introduced to organizations, the need for network and communication technologies to provide an easy integration of knowledge and information sharing is essential. Network and Communication Technology Innovations for Web and IT Advancement presents studies on trends, developments, and methods on information technology advancements through network and communication technology. This collection brings together integrated approaches for communication technology and usage for web and IT advancements.
Download or read book Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach written by Ehsan Sobhani-Tehrani and published by Springer Science & Business Media. This book was released on 2009-06-22 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theincreasingcomplexityofspacevehiclessuchassatellites,andthecostreduction measures that have affected satellite operators are increasingly driving the need for more autonomy in satellite diagnostics and control systems. Current methods for detecting and correcting anomalies onboard the spacecraft as well as on the ground are primarily manual and labor intensive, and therefore, tend to be slow. Operators inspect telemetry data to determine the current satellite health. They use various statisticaltechniques andmodels,buttheanalysisandevaluation ofthelargevolume of data still require extensive human intervention and expertise that is prone to error. Furthermore, for spacecraft and most of these satellites, there can be potentially unduly long delays in round-trip communications between the ground station and the satellite. In this context, it is desirable to have onboard fault-diagnosis system that is capable of detecting, isolating, identifying or classifying faults in the system withouttheinvolvementandinterventionofoperators.Towardthisend,theprinciple goal here is to improve the ef?ciency, accuracy, and reliability of the trend analysis and diagnostics techniques through utilization of intelligent-based and hybrid-based methodologies.
Download or read book Linear Multivariable Control written by W. M. Wonham and published by Springer Science & Business Media. This book was released on 2013-11-21 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: In writing this monograph my objective is to present arecent, 'geometrie' approach to the structural synthesis of multivariable control systems that are linear, time-invariant, and of finite dynamic order. The book is addressed to graduate students specializing in control, to engineering scientists engaged in control systems research and development, and to mathematicians with some previous acquaintance with control problems. The label 'geometrie' is applied for several reasons. First and obviously, the setting is linear state space and the mathematics chiefly linear algebra in abstract (geometrie) style. The basic ideas are the familiar system concepts of controllability and observability, thought of as geometrie properties of distinguished state subspaces. Indeed, the geometry was first brought in out of revulsion against the orgy of matrix manipulation which linear control theory mainly consisted of, not so long ago. But secondlyand of greater interest, the geometrie setting rather quickly suggested new methods of attacking synthesis which have proved to be intuitive and economical; they are also easily reduced to matrix arith metic as soonas you want to compute. The essence of the 'geometrie' approach is just this: instead of looking directly for a feedback laW (say u = Fx) which would solve your synthesis problem if a solution exists, first characterize solvability as a verifiable property of some constructible state subspace, say J. Then, if all is weIl, you may calculate F from J quite easily.
Download or read book Power System State Estimation written by Ali Abur and published by CRC Press. This book was released on 2004-03-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering an up-to-date account of the strategies utilized in state estimation of electric power systems, this text provides a broad overview of power system operation and the role of state estimation in overall energy management. It uses an abundance of examples, models, tables, and guidelines to clearly examine new aspects of state estimation, the testing of network observability, and methods to assure computational efficiency. Includes numerous tutorial examples that fully analyze problems posed by the inclusion of current measurements in existing state estimators and illustrate practical solutions to these challenges. Written by two expert researchers in the field, Power System State Estimation extensively details topics never before covered in depth in any other text, including novel robust state estimation methods, estimation of parameter and topology errors, and the use of ampere measurements for state estimation. It introduces various methods and computational issues involved in the formulation and implementation of the weighted least squares (WLS) approach, presents statistical tests for the detection and identification of bad data in system measurements, and reveals alternative topological and numerical formulations for the network observability problem.
Download or read book Stability Analysis and State Estimation of Memristive Neural Networks written by Hongjian Liu and published by CRC Press. This book was released on 2024-10-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the stability analysis and estimator design problems for discrete-time memristive neural networks subject to time-delays and approaches state estimation from different perspectives. Each chapter includes analysis problems and application of theories and techniques to pertinent research areas.
