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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.
Event-based systems are a class of reactive systems deployed in a wide spectrum of engineering disciplines including control, communication, signal processing, and electronic instrumentation. Activities in event-based systems are triggered in response to events usually representing a significant change of the state of controlled or monitored physical variables. Event-based systems adopt a model of calls for resources only if it is necessary, and therefore, they are characterized by efficient utilization of communication bandwidth, computation capability, and energy budget. Currently, the economical use of constrained technical resources is a critical issue in various application domains because many systems become increasingly networked, wireless, and spatially distributed. Event-Based Control and Signal Processing examines the event-based paradigm in control, communication, and signal processing, with a focus on implementation in networked sensor and control systems. Featuring 23 chapters contributed by more than 60 leading researchers from around the world, this book covers: Methods of analysis and design of event-based control and signal processing Event-driven control and optimization of hybrid systems Decentralized event-triggered control Periodic event-triggered control Model-based event-triggered control and event-triggered generalized predictive control Event-based intermittent control in man and machine Event-based PID controllers Event-based state estimation Self-triggered and team-triggered control Event-triggered and time-triggered real-time architectures for embedded systems Event-based continuous-time signal acquisition and DSP Statistical event-based signal processing in distributed detection and estimation Asynchronous spike event coding technique with address event representation Event-based processing of non-stationary signals Event-based digital (FIR and IIR) filters Event-based local bandwidth estimation and signal reconstruction Event-Based Control and Signal Processing is the first extensive study on both event-based control and event-based signal processing, presenting scientific contributions at the cutting edge of modern science and engineering.
This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, sufficient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations. Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspective Systematically introduces the complex sampling concept, methods, and application for the control and state estimation Presents unified framework for control and state estimation problems of dynamical network systems with complex samplings Exploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approach Explains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specifications This book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.
Event-based control is a means to restrict the feedback in control loops to event time instants that are determined by a well-defined triggering mechanism. The aim of this control strategy is to adapt the communication over the feedback link to the system behavior. In this thesis, a state-feedback approach to event-based control is extended to systems that are composed of physically interconnected subsystems. The main concern of this thesis is disturbance rejection in interconnected systems, which is supposed to be best accomplished by a continuous state feedback. This consideration leads to the idea that the event-based state-feedback system should approximate the disturbance rejection behavior of a continuous state-feedback system with adjustable precision. Various methods for the event-based control of physically interconnected systems are investigated. In particular, decentralized, distributed and centralized state feedback is studied, which differ with respect to the effort for the communication between the components of the event-based controller over the communication network. The main results concern the design and analysis of event-based state-feedback control methods for physically interconnected systems. For all approaches the disturbance behavior of a continuous state-feedback system is shown to be approximated with adjustable accuracy by the event-based state-feedback system. The novel event-based control methods are tested and evaluated in experiments on a continuous flow process implemented on a large-scale pilot plant.
The objective of this book is to present the up-to-date research developments and novel methodologies on state estimation and fault diagnosis (FD) techniques for a class of complex systems subject to closed-loop control, nonlinearities, and stochastic phenomena. It covers state estimation design methodologies and FD unit design methodologies including framework of optimal filter and FD unit design, robust filter and FD unit design, stability, and performance analysis for the considered systems subject to various kinds of complex factors. Features: Reviews latest research results on the state estimation and fault diagnosis issues. Presents comprehensive framework constituted for systems under imperfect measurements. Includes quantitative performance analyses to solve problems in practical situations. Provides simulation examples extracted from practical engineering scenarios. Discusses proper and novel techniques such as the Carleman approximation and completing the square method is employed to solve the mathematical problems. This book aims at Graduate students, Professionals and Researchers in Control Science and Application, Stochastic Process, Fault Diagnosis, and Instrumentation and Measurement.
A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.
This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.
This book presents research developments and novel methodologies on data-rate-constrained control and state estimation for complex networked systems with different kinds of encoding-decoding mechanisms. It describes framework of state estimator and controller design, stability and performance analysis for data-rate constrained complex systems with various kinds of encoding-decoding schemes and so forth. Simulations given in this book are constructed by applying MATLAB® software package. Features: Gives a systematic investigation of the control and state estimation for complex networked systems subject to the data rate constraint. Develops control/filtering algorithms in a unified framework. Includes comparisons for different coding-decoding techniques proposed. Discusses theoretical value and practical application for the resource-constrained communication environment. Provides performance analysis as well as the parameterizations of filters and FD units. This book is aimed at researchers and graduate students in electrical engineering, signal processing, control systems and complex networks.
This contributed volume aims to build the foundation of a framework for computationally aware algorithmic design for cyber-physical systems (CPSs), focusing on approaches that take computation into account at the design stage to address their impact on performance and safety. It demonstrates how novel techniques may emerge from the combination of formal methods, model predictive control, distributed optimization, data-driven methods, reconfigurable/adaptive methods, and information-theoretic techniques. Chapters are written by both researchers and practitioners and cover such topics as analysis and design of uncertain CPSs, cooperative and non-cooperative paradigms for handling complexity in large scale CPSs, task-relevant environment abstractions for autonomous systems based on information theory, information flow in event-based stabilization of CPSs, set-valued model predictive control, and automated synthesis of certifiable controllers for CPSs. State-of-the-art applications and case studies are provided throughout with a special focus on intelligent transportation systems and autonomous vehicles. Graduate students and researchers with an interest in CPS verification and control will find this volume to be a valuable resource in their work. It will also appeal to researchers from disciplines other than control, such as computer science, operations research, applied mathematics, and robotics.
Papers from a flagship conference reflect the latest developments in the field, including work in such rapidly advancing areas as human-robot interaction and formal methods. Robotics: Science and Systems VII spans a wide spectrum of robotics, bringing together researchers working on the algorithmic or mathematical foundations of robotics, robotics applications, and analysis of robotics systems. This volume presents the proceedings of the seventh annual Robotics: Science and Systems conference, held in 2011 at the University of Southern California. The papers presented cover a wide range of topics in robotics, spanning mechanisms, kinematics, dynamics and control, human-robot interaction and human-centered systems, distributed systems, mobile systems and mobility, manipulation, field robotics, medical robotics, biological robotics, robot perception, and estimation and learning in robotic systems. The conference and its proceedings reflect not only the tremendous growth of robotics as a discipline but also the desire in the robotics community for a flagship event at which the best of the research in the field can be presented.