This monograph introduces methods for handling filtering and control problems in nonlinear stochastic systems arising from network-induced phenomena consequent on limited communication capacity. Such phenomena include communication delay, packet dropout, signal quantization or saturation, randomly occurring nonlinearities and randomly occurring uncertainties. The text is self-contained, beginning with an introduction to nonlinear stochastic systems, network-induced phenomena and filtering and control, moving through a collection of the latest research results which focuses on the three aspects of: · the state-of-the-art of nonlinear filtering and control; · recent advances in recursive filtering and sliding mode control; and · their potential for application in networked control systems, and concluding with some ideas for future research work. New concepts such as the randomly occurring uncertainty and the probability-constrained performance index are proposed to make the network models as realistic as possible. The power of combinations of such recent tools as the completing-the-square and sums-of-squares techniques, Hamilton‒Jacobi‒Isaacs matrix inequalities, difference linear matrix inequalities and parameter-dependent matrix inequalities is exploited in treating the mathematical and computational challenges arising from nonlinearity and stochasticity. Nonlinear Stochastic Systems with Network-Induced Phenomena establishes a unified framework of control and filtering which will be of value to academic researchers in bringing structure to problems associated with an important class of networked system and offering new means of solving them. The significance of the new concepts, models and methods presented for practical control engineering and signal processing will also make it a valuable reference for engineers dealing with nonlinear control and filtering problems.

Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: • a unified framework for filtering and control problems in complex communication networks with limited bandwidth; • new concepts such as random sensor and signal saturations for more realistic modeling; and • demonstration of the use of techniques such as the Hamilton–Jacobi–Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book.

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 information Investigates several new concepts for randomly occurring phenomena and proposes a new system model to better describe network-induced problems Demonstrates how newly developed techniques can handle emerging mathematical and computational challenges Contains the latest research results Filtering, 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.

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

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

This book establishes a unified framework for dealing with typical engineering complications arising in modern, complex, large-scale networks such as parameter uncertainties, missing measurement and cyber-attack. Distributed Filtering, Control and Synchronization is a timely reflection on methods designed to handle a series of control and signal-processing issues in modern industrial engineering practice in areas like power grids and environmental monitoring. It exploits the latest techniques to handle the emerging mathematical and computational challenges arising from, among other things, the dynamic topologies of distributed systems and in the context of sensor networks and multi-agent systems. These techniques include recursive linear matrix inequalities, local-performance and stochastic analyses and techniques based on matrix theory. Readers interested in the theory and application of control and signal processing will find much to interest them in the new models and methods presented in this book. Academic researchers can find ideas for developing their own research, graduate and advanced undergraduate students will be made aware of the state of the art, and practicing engineers will find methods for addressing practical difficulties besetting modern networked 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 results This 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.

Stochastic processes have wide relevance in mathematics both for theoretical aspects and for their numerous real-world applications in various domains. They represent a very active research field which is attracting the growing interest of scientists from a range of disciplines. This Special Issue aims to present a collection of current contributions concerning various topics related to stochastic processes and their applications. In particular, the focus here is on applications of stochastic processes as models of dynamic phenomena in research areas certain to be of interest, such as economics, statistical physics, queuing theory, biology, theoretical neurobiology, and reliability theory. Various contributions dealing with theoretical issues on stochastic processes are also included.

This book constitutes the thoroughly refereed proceedings of the themed workshops of the 6th International Conference on Life System Modeling and Simulation, LSMS 2020, and of the 6th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2020, held in Hangzhou, China, in October 2020. The 36 full and 2 short papers presented were carefully reviewed and selected from over 165 submissions. The papers of this volume are organized in topical sections on: smart energy systems and devices; intelligent manufacturing and systems; and intelligent biology and information systems.

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.