Zidong Wang – författare

The book addresses the system performance with a focus on the network-enhanced complexities and developing the engineering-oriented design framework of controllers and filters with potential applications in system sciences, control engineering and signal processing areas. Therefore, it provides a unified treatment on the analysis and synthesis for discrete-time stochastic systems with guarantee of certain performances against network-enhanced complexities with applications in sensor networks and mobile robotics. Such a result will be of great importance in the development of novel control and filtering theories including industrial impact.Key Features Provides original methodologies and emerging concepts to deal with latest issues in the control and filtering with an emphasis on a variety of network-enhanced complexities Gives results of stochastic control and filtering distributed control and filtering, and security control of complex networked systems Captures the essence of performance analysis and synthesis for stochastic control and filtering Concepts and performance indexes proposed reflect the requirements of engineering practice Methodologies developed in this book include backward recursive Riccati difference equation approach and the discrete-time version of input-to-state stability in probability

Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for stochastic systems with traditional and emerging engineering-oriented complexities. The book begins with an overview of the relevant background, motivation, and research problems, and then:Discusses the robust stability and stabilization problems for a class of stochastic time-delay interval systems with nonlinear disturbancesInvestigates the robust stabilization and H∞ control problems for a class of stochastic time-delay uncertain systems with Markovian switching and nonlinear disturbancesExplores the H∞ state estimator and H∞ output feedback controller design issues for stochastic time-delay systems with nonlinear disturbances, sensor nonlinearities, and Markovian jumping parametersAnalyzes the H∞ performance for a general class of nonlinear stochastic systems with time delays, where the addressed systems are described by general stochastic functional differential equationsStudies the filtering problem for a class of discrete-time stochastic nonlinear time-delay systems with missing measurement and stochastic disturbancesUses gain-scheduling techniques to tackle the probability-dependent control and filtering problems for time-varying nonlinear systems with incomplete informationEvaluates the filtering problem for a class of discrete-time stochastic nonlinear networked control systems with multiple random communication delays and random packet lossesExamines the filtering problem for a class of nonlinear genetic regulatory networks with state-dependent stochastic disturbances and state delaysConsiders the H∞ state estimation problem for a class of discrete-time complex networks with probabilistic missing measurements and randomly occurring coupling delaysAddresses the H∞ synchronization control problem for a class of dynamical networks with randomly varying nonlinearitiesNonlinear Stochastic Control and Filtering with Engineering-oriented Complexities describes novel methodologies that can be applied extensively in lab simulations, field experiments, and real-world engineering practices. Thus, this text provides a valuable reference for researchers and professionals in the signal processing and control engineering communities.

In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly occurring sensor failures and consensus in probability are discussed. Finally, the theories/techniques developed are applied to emerging research areas.Key Features Unifies existing and emerging concepts concerning stochastic control/filtering and distributed control/filtering with an emphasis on a variety of network-induced complexities Includes concepts like randomly occurring sensor failures and consensus in probability (with respect to time-varying stochastic multi-agent systems) Exploits the recursive linear matrix inequality approach, completing the square method, Hamilton-Jacobi inequality approach, and parameter-dependent matrix inequality approach to handle the emerging mathematical/computational challenges Captures recent advances of theories, techniques, and applications of stochastic control as well as filtering from an engineering-oriented perspective Gives simulation examples in each chapter to reflect the engineering practice

In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated within a unified theoretical framework. In this stage, some novel notions are put forward to reflect the engineering practice. Then, the estimator design issues are discussed where sufficient conditions are derived to ensure the existence of the desired estimators with guaranteed performances. Finally, the theories and techniques developed in previous parts are applied to deal with some issues in several emerging research areas.The book Unifies existing and emerging concepts concerning delayed discrete memristive neural networks with an emphasis on a variety of network-induced phenomena Captures recent advances of theories, techniques, and applications of delayed discrete memristive neural networks from a network-oriented perspective Provides a series of latest results in two popular yet interrelated areas, stability analysis and state estimation of neural networks Exploits a unified framework for analysis and synthesis by designing new tools and techniques in combination with conventional theories of systems science, control engineering and signal processing Gives simulation examples in each chapter to reflect the engineering practice

In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated within a unified theoretical framework. In this stage, some novel notions are put forward to reflect the engineering practice. Then, the estimator design issues are discussed where sufficient conditions are derived to ensure the existence of the desired estimators with guaranteed performances. Finally, the theories and techniques developed in previous parts are applied to deal with some issues in several emerging research areas.The book Unifies existing and emerging concepts concerning delayed discrete memristive neural networks with an emphasis on a variety of network-induced phenomena Captures recent advances of theories, techniques, and applications of delayed discrete memristive neural networks from a network-oriented perspective Provides a series of latest results in two popular yet interrelated areas, stability analysis and state estimation of neural networks Exploits a unified framework for analysis and synthesis by designing new tools and techniques in combination with conventional theories of systems science, control engineering and signal processing Gives simulation examples in each chapter to reflect the engineering practice

This book presents up-to-date research developments and novel methodologies regarding recursive filtering for 2-D shift-varying systems with various communication constraints. It investigates recursive filter/estimator design and performance analysis by a combination of intensive stochastic analysis, recursive Riccati-like equations, variance-constrained approach, and mathematical induction. Each chapter considers dynamics of the system, subtle design of filter gains, and effects of the communication constraints on filtering performance. Effectiveness of the derived theories and applicability of the developed filtering strategies are illustrated via simulation examples and practical insight.Features:- Covers recent advances of recursive filtering for 2-D shift-varying systems subjected to communication constraints from the engineering perspective. Includes the recursive filter design, resilience operation and performance analysis for the considered 2-D shift-varying systems. Captures the essence of the design for 2-D recursive filters. Develops a series of latest results about the robust Kalman filtering and protocol-based filtering. Analyzes recursive filter design and filtering performance for the considered systems.This book aims at graduate students and researchers in mechanical engineering, industrial engineering, communications networks, applied mathematics, robotics and control systems.

This book presents up-to-date research developments and novel methodologies regarding recursive filtering for 2-D shift-varying systems with various communication constraints. It investigates recursive filter/estimator design and performance analysis by a combination of intensive stochastic analysis, recursive Riccati-like equations, variance-constrained approach, and mathematical induction. Each chapter considers dynamics of the system, subtle design of filter gains, and effects of the communication constraints on filtering performance. Effectiveness of the derived theories and applicability of the developed filtering strategies are illustrated via simulation examples and practical insight.Features:- Covers recent advances of recursive filtering for 2-D shift-varying systems subjected to communication constraints from the engineering perspective. Includes the recursive filter design, resilience operation and performance analysis for the considered 2-D shift-varying systems. Captures the essence of the design for 2-D recursive filters. Develops a series of latest results about the robust Kalman filtering and protocol-based filtering. Analyzes recursive filter design and filtering performance for the considered systems.This book aims at graduate students and researchers in mechanical engineering, industrial engineering, communications networks, applied mathematics, robotics and control systems.

Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis copes with the filter design, fault estimation and reliable control problems for different classes of nonlinear stochastic time-varying systems with network-enhanced complexities. Divided into three parts, the book discusses the finite-horizon filtering, fault estimation and reliable control, and randomly occurring nonlinearities/uncertainties followed by designing of distributed state and fault estimators, and distributed filters. The third part includes problems of variance-constrained H∞ state estimation, partial-nodes-based state estimation and recursive filtering for nonlinear time-varying complex networks with randomly varying topologies, and random coupling strengths. Offers a comprehensive treatment of the topics related to Networked Nonlinear Stochastic Time-Varying Systems with rigorous math foundation and derivation Unifies existing and emerging concepts concerning control/filtering/estimation and distributed filtering Provides a series of latest results by drawing on the conventional theories of systems science, control engineering and signal processing Deal with practical engineering problems such as event triggered H∞ filtering, non-fragile distributed estimation, recursive filtering, set-membership filtering Demonstrates illustrative examples in each chapter to verify the correctness of the proposed resultsThis book is aimed at engineers, mathematicians, scientists, and upper-level students in the fields of control engineering, signal processing, networked control systems, robotics, data analysis, and automation.

Networked Non-linear Stochastic Time-Varying Systems: Analysis and Synthesis copes with the filter design, fault estimation and reliable control problems for different classes of nonlinear stochastic time-varying systems with network-enhanced complexities. Divided into three parts, the book discusses the finite-horizon filtering, fault estimation and reliable control, and randomly occurring nonlinearities/uncertainties followed by designing of distributed state and fault estimators, and distributed filters. The third part includes problems of variance-constrained H∞ state estimation, partial-nodes-based state estimation and recursive filtering for nonlinear time-varying complex networks with randomly varying topologies, and random coupling strengths. Offers a comprehensive treatment of the topics related to Networked Nonlinear Stochastic Time-Varying Systems with rigorous math foundation and derivation Unifies existing and emerging concepts concerning control/filtering/estimation and distributed filtering Provides a series of latest results by drawing on the conventional theories of systems science, control engineering and signal processing Deal with practical engineering problems such as event triggered H∞ filtering, non-fragile distributed estimation, recursive filtering, set-membership filtering Demonstrates illustrative examples in each chapter to verify the correctness of the proposed resultsThis book is aimed at engineers, mathematicians, scientists, and upper-level students in the fields of control engineering, signal processing, networked control systems, robotics, data analysis, and automation.

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 perspectiveSystematically introduces the complex sampling concept, methods, and application for the control and state estimationPresents unified framework for control and state estimation problems of dynamical network systems with complex samplingsExploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approachExplains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specificationsThis book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

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 perspectiveSystematically introduces the complex sampling concept, methods, and application for the control and state estimationPresents unified framework for control and state estimation problems of dynamical network systems with complex samplingsExploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approachExplains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specificationsThis book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.

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.

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.

This book discusses the Sliding Mode Control (SMC) problems of networked control systems (NCSs) under various communication protocols including static/dynamic/periodic event-triggered mechanism, and stochastic communication, Round-Robin, weighted try-once-discard, multiple-packet transmission, and the redundant channel transmission protocol. The super-twisting algorithm and the extended-state-observer-based SMC scheme are described in this book for suppressing chattering. Besides, the SMC designs for two-dimensional (1-D) and two-dimensional (2-D) NCSs are illustrated as well.Features: Captures recent advances of theories, techniques, and applications of networked sliding mode control from an engineering-oriented perspective. Includes new design ideas and optimization techniques of networked sliding mode control theory. Provides advanced tools to apply networked sliding mode control techniques in the practical applications. Discusses some new tools to the engineering applications while dealing with the model uncertainties and external disturbances.This book aims at Researchers and professionals in Control Systems, Computer Networks, Internet of Things, and Communication Systems.

This book discusses the Sliding Mode Control (SMC) problems of networked control systems (NCSs) under various communication protocols including static/dynamic/periodic event-triggered mechanism, and stochastic communication, Round-Robin, weighted try-once-discard, multiple-packet transmission, and the redundant channel transmission protocol. The super-twisting algorithm and the extended-state-observer-based SMC scheme are described in this book for suppressing chattering. Besides, the SMC designs for two-dimensional (1-D) and two-dimensional (2-D) NCSs are illustrated as well.Features: Captures recent advances of theories, techniques, and applications of networked sliding mode control from an engineering-oriented perspective. Includes new design ideas and optimization techniques of networked sliding mode control theory. Provides advanced tools to apply networked sliding mode control techniques in the practical applications. Discusses some new tools to the engineering applications while dealing with the model uncertainties and external disturbances.This book aims at Researchers and professionals in Control Systems, Computer Networks, Internet of Things, and Communication Systems.

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning. Features: Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities. Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective. Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system. Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems. Includes simulation examples in each chapter to reflect the engineering practice.This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning. Features: Unifies existing and emerging concepts concerning robust filtering and fault diagnosis with an emphasis on a variety of network-induced complexities. Explains theories, techniques, and applications of state estimation as well as fault diagnosis from an engineering-oriented perspective. Provides a series of latest results in robust/stochastic filtering, multidate sample, and time-varying system. Captures diagnosis (fault detection, fault isolation and fault estimation) for time-varying multi-rate systems. Includes simulation examples in each chapter to reflect the engineering practice.This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.

This book presents the up-to-date research developments and novel methodologies on multi-sensor filtering fusion (MSFF) for a class of complex systems subject to censored data under a constrained network environment. The contents of this book are divided into two parts covering centralized and distributed MSFF design methodologies. The work provides a framework of optimal centralized/distributed filter design and stability and performance analysis for the considered systems along with designed filters. Simulations presented in this book are implemented using MATLAB.Features:Includes concepts, backgrounds and models on censored data, filtering fusion and communication constraints.Reviews case studies to provide clear engineering insights into the developed fusion theories and techniques.Provides theoretic values and engineering insights of the censored data and constrained network.Discusses performance evaluation of the presented multi-sensor fusion algorithms.Explores promising research directions on future multi-sensor fusion.This book is aimed at graduate students and researchers in networked control, sensor networks, and data fusion.

This book presents novel state estimation methods for several classes of networked multi-rate systems including state estimation methods for networked multi-rate systems with various complex networked-induced phenomena and communication protocols. The systems investigated include stochastic nonlinear systems, time-delay systems, linear repetitive processes, and artificial neural networks. The techniques used are mainly the Lyapunov stability theory, the optimal estimation theory, the lifting technique, and certain convex optimization method.FeaturesGives a systematic investigation of the state estimation of multi-rate systemsDiscusses results on state estimation problems under network-induced complexitiesStudies different kinds of multi-rate systems including multi-rate nonlinear systems, multi-rate neural networks, and multi-rate linear repetitive processesExplores network-enhanced complexities and communication protocolsIncludes case studies showing the applicability of developed estimation algorithms including practical examples like DC servo systems and continuous stirred tank reactor systemsAnalysis and Synthesis for Networked Multi-Rate Systems is aimed at graduate students and researchers in signal processing, control systems, and electrical engineering.

This book presents novel state estimation methods for several classes of networked multi-rate systems including state estimation methods for networked multi-rate systems with various complex networked-induced phenomena and communication protocols. The systems investigated include stochastic nonlinear systems, time-delay systems, linear repetitive processes, and artificial neural networks. The techniques used are mainly the Lyapunov stability theory, the optimal estimation theory, the lifting technique, and certain convex optimization method.FeaturesGives a systematic investigation of the state estimation of multi-rate systemsDiscusses results on state estimation problems under network-induced complexitiesStudies different kinds of multi-rate systems including multi-rate nonlinear systems, multi-rate neural networks, and multi-rate linear repetitive processesExplores network-enhanced complexities and communication protocolsIncludes case studies showing the applicability of developed estimation algorithms including practical examples like DC servo systems and continuous stirred tank reactor systemsAnalysis and Synthesis for Networked Multi-Rate Systems is aimed at graduate students and researchers in signal processing, control systems, and electrical engineering.

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 book presents advanced Dynamic State Estimation (DSE) methodologies across three power system classes: quasi-steady power systems, synchronous-generator-based multi-machine systems and renewable energy microgrids. The work tackles unconventional measurements including time-delays, multi-rate measurements, event-triggered transmissions, outliers, and sensor saturations including complex factors. The nine-chapter structure progresses from fundamentals through specific applications to future research directions.Explore state estimation in power systems and challenges in large, complex grids with high renewable energy integration.Provides step-by-step derivations of theoretical results and case studies based on widely used power system benchmarks.Addresses two prominent research and technological aspects: soft computing for estimation and power electrical systems.Considers practical issues such as online application and computational efficiency in the estimator design,Reviews analysis techniques such as optimal estimation technique, completing the square method, stochastic analysis technique, and entropy theoryThis book is aimed at graduate students and researchers in electrical engineering, power systems, control systems, and signal processing.

Recursive Filtering of Networked Systems with Communication Protocol Schedulingexplores protocol-based state estimation for complex networked systems including Kalman filtering for nonlinear systems under RR and MEF-TOD protocols, finite-horizon robust state estimation under FlexRay protocol, robust filtering for multi-rate systems under Round-Robin and stochastic communication protocols, and state estimation under event-triggering protocol and redundant channel for neural networks. This book provides theoretical frameworks using techniques like backward Riccati equations, Kalman filtering theory, Unscented transform, and ellipsoid estimation theory.Features1 Focuses on the intersection of recursive filtering techniques and communication protocol scheduling 2 Introduces communication protocols to allocate limited network resources 3 Enhances the reliability and accuracy of information transmission and minimize the occurrence of information conflicts and congestion in the network 4 Offers practical strategies for real-world challenges in networked systems 5 Explains designing filtering unit for a class of complex networksThis book is aimed at graduate students and researchers in electrical engineering, power systems, control systems.

The four novel Dynamic Event-Triggered Mechanisms (DETMs) in this book are for complex networked systems that optimize resource utilization while preserving system performance. These include hybrid dynamic variables-dependent ETMs, hybrid adjusting variables-dependent ETMs, bounded dynamic variable/time-varying threshold-dependent ETMs, and terminal constraint set-dependent mixed time/event-triggered mechanisms. It explores applications across fault diagnosis in networked systems, finite-time state estimation for complex dynamical networks, and MPC implementation.Presents a systematic study on dynamic event-triggered mechanismsReviews comprehensive research results on dynamic event-triggered fault diagnosis, state estimation, and MPCExplores how these theories can be applied to the practical engineering problem of load frequency control in power systemsDiscusses complex factors including cyber-attacks, multiple timescales, jumping system parameters, hard constraints, polytopic uncertainties, nonlinearities, and limited network/computing resourcesIncludes numerous simulations and examples to validate the theoretical results including single-link rigid robot model systems, motor systems, and power systems.This book is aimed at graduate students and researchers in control systems, computer sciences, and signal processing.

This book explores PID control and PI state estimation for complex networked systems facing network-enhanced challenges including fading measurements, time-delays, packet dropouts, and cyber-attacks including PID control problems, PI state estimation and neural networks.It addresses output-feedback control, observer-based control, tracking control, and state estimation across different network conditions, implementing solutions for resource constraints, outlier resistance, and partial measurement scenarios using MATLAB simulations.Key features of the book-1 Presents systematic research on the PID control problem based on the state space method for networked systems 2 Contains everything needed to capture the essence of design and performance evaluation for PID controllers and PI estimators 3 Covers security aspects of encoded transmissions, encompassing encryption protocols and cyber-physical attack resilience 4 Addresses issues such as network-induced complexities, cybersecurity threats, and adaptive control strategies 5 Emphasizes flexible, reliable, and practical solutions for real-world systems, bridging theoretical rigor with engineering applications This book is aimed at graduate students and researchers in control science and engineering, signal processing, computer science.

Model Predictive Control (MPC) has advanced as a robust method for managing complex dynamic systems, surpassing traditional control strategies in performance and constraint handling. This book explores MPC theory and applications, focusing on robust MPC (RMPC) for systems with uncertainties. It examines three system types: networked systems, stochastic switching systems, and nonlinear hybrid systems. It addresses challenges in networked interventions, Markovian jump systems, and nonlinear systems under communication constraints.• Integrates model predictive control, network-induced constraints, cyber-security issues, and advanced communication protocols• Covers control and state estimation with a focus on dynamic network systems with complex sampling• Considers and models network-induced complexities• Employs several analysis techniques to overcome the recent mathematical/computational difficulties for discrete-time systems• Deals with practical engineering problems for complex dynamic systems with different kinds of scenario-induced complexities or frame-work-induced complexitiesThis book is aimed at graduate students and researchers in networks, signal processing, controls, and dynamic complex systems.

In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modelling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent.Filtering, Control and Fault Detection with Randomly Occurring Incomplete Information reflects the state-of-the-art of the research area for handling randomly occurring incomplete information from three interrelated aspects of control, filtering and fault detection. Recent advances in networked control systems and distributed filtering over sensor networks are covered, and application potential in mobile robotics is also considered. The reader will benefit from the introduction of new concepts, new models and new methodologies with practical significance in control engineering and signal processing. Key Features: Establishes a unified framework for filtering, control and fault detection problem for various discrete-time nonlinear stochastic systems with randomly occurring incomplete informationInvestigates several new concepts for randomly occurring phenomena and proposes a new  system model to better describe network-induced problemsDemonstrates how newly developed techniques can handle emerging mathematical and    computational challengesContains the latest research resultsFiltering, Control and Fault Detection with Randomly Occurring Incomplete Information provides a unified yet neat framework for control/filtering/fault-detection with randomly occurring incomplete information.  It is a comprehensive textbook for graduate students and is also a useful practical research reference for engineers dealing with control, filtering and fault detection problems for networked systems.

Unifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomenaEstablishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performanceIncludes case studies of several nonlinear stochastic systemsInvestigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturationsConsiders both time-invariant systems and time-varying systemsExploits newly developed techniques to handle the emerging mathematical and computational challenges

To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to make this decoupling a reality. The proposed system integrates and extends traditional analytical-based methods and custom-designed novel algorithms.The book first explores a new technique that takes advantage of a multiview approach to image analysis and addresses the challenges of applying powerful traditional techniques, such as clustering, to full-scale microarray experiments. It then presents an effective feature identification approach, an innovative technique that renders highly detailed surface models, a new approach to subgrid detection, a novel technique for the background removal process, and a useful technique for removing "noise." The authors also develop an expectation–maximization (EM) algorithm for modeling gene regulatory networks from gene expression time series data. The final chapter describes the overall benefits of these techniques in the biological and computer sciences and reviews future research topics.This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas.