This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled.
The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates. It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control.
Key features and topics include:
Model-Based Control of Networked Systems will appeal to researchers, practitioners, and graduate students interested in the control of networked systems, distributed systems, and systems with limited feedback.
This monograph provides the reader with a systematic treatment of robust filter design, a key issue in systems, control and signal processing, because of the fact that the inevitable presence of uncertainty in system and signal models often degrades the filtering performance and may even cause instability. The methods described are therefore not subject to the rigorous assumptions of traditional Kalman filtering. The monograph is concerned with robust filtering for various dynamical systems with parametric uncertainties and focuses on parameter-dependent approaches to filter design. Classical filtering schemes, like H2 filtering and HÂ¥ filtering, are addressed and emerging issues such as robust filtering with constraints on communication channels and signal frequency characteristics are discussed. The text features:
Â· design approaches to robust filters arranged according to varying complexity level and emphasizing robust filtering in the parameter-dependent framework for the first time;
Â· guidance on the use of special realistic phenomena or factors to describe problems more accurately and to improve filtering performance;
Â· a unified linear matrix inequality formulation of design approaches for easy and effective filter design;
Â· demonstration of the techniques of matrix decoupling technique, the generalized Kalman?Yakubovich?Popov lemma, the free weighting matrix technique and the delay modelling approach, in robust filtering;
Â· numerous easy-to-follow simulation examples, graphical and tabular illustrations to help the reader understand the filter design approaches developed; and
Â· an account of emerging issues on robust filtering for research to inspire future investigation.
Robust Filtering for Uncertain Systems will be of interest to academic researchers specializing in linear, robust and optimal control and estimation and to practitioners working in tracking and network control or signal filtering, detection and estimation. Graduate students learning control and systems theory, signal processing or applied mathematics will also find the book to be a valuable resource.
Information is always required by organizations of coastal states about the movements, identities and intentions of vessels sailing in the waters of interest to them, which may be coastal waters, straits, inland waterways, rivers, lakes or open seas. This interest may stem from defense requirements or from needs for the protection of off-shore resources, enhanced search and rescue services, deterrence of smuggling, drug trafficking and other illegal activities and/or for providing vessel traffic services for safe and efficient navigation and protection of the environment.
A practical approach to estimating and tracking dynamic systems in real-worl applications
Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices.
Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.
Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLABÂ® toolbox of estimation methods.
Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.
The death of Professor Arthur Wright in the summer of 1996 deprived me of a friend and a colleague whose judgement and experience shaped this book. I pay tribute to his contributions to protection and electrical engineering education. In the five years since the first edition appeared, many developments have taken place and it is now necessary to update the book. The use of digital communications and advanced signal processing techniques is now widespread and several fully numeric relays are available from manu- facturers. Two new Chapters 13 and 14 have been added to introduce readers to these concepts and associated techniques. Artificial intelligence is making its impact in all engineering applications and power system protection is no exception. Expert systems, fuzzy logic, artificial neural networks, adaptive and integrated protection, synchronized measurements using the global positioning system, genetic algorithms, flexible a.c. transmission systems, are some of the techniques considered in connection with protection. Although many of these techniques have not yet found major application in protection, it is nevertheless essential for the educated protection engineer to have a basic understanding of the underlying principles and methodology so that he, or she, can evaluate their suitability for new relaying problems and applications. Chapter 15 was therefore added to guide readers through this developing area. I have also added some new material in other chapters to reflect changes over the past years.
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