This book is concerned with the fault estimation problem for network systems. Firstly, to improve the existing adaptive fault estimation observer, a novel so-called intermediate estimator is proposed to identify the actuator or sensor faults in dynamic control systems with high accuracy and convergence speed. On this basis, by exploiting the properties of network systems such as multi-agent systems and large-scale interconnected systems, this book introduces the concept of distributed intermediate estimator; faults in different nodes can be estimated simultaneously; meanwhile, satisfactory consensus performances can be obtained via compensation based protocols. Finally, the characteristics of the new fault estimation methodology are verified and discussed by a series of experimental results on networked multi-axis motion control systems. This book can be used as a reference book for researcher and designer in the field of fault diagnosis and fault-tolerant control and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities.

The book focuses on new theoretical results and techniques in the field of intelligent systems and control. It provides in-depth studies on a number of major topics such as Multi-Agent Systems, Complex Networks, Intelligent Robots, Complex System Theory and Swarm Behavior, Event-Triggered Control and Data-Driven Control, Robust and Adaptive Control, Big Data and Brain Science, Process Control, Intelligent Sensor and Detection Technology, Deep learning and Learning Control Guidance, Navigation and Control of Flight Vehicles and so on. Given its scope, the book will benefit all researchers, engineers, and graduate students who want to learn about cutting-edge advances in intelligent systems, intelligent control, and artificial intelligence.

The book focuses on the research methods of networked control systems via sliding mode. The problems with network disturbances, network induced delay, out-of-sequence and packet loss, and network attacks are studied in detail. The content studied in this book is introduced in detail and is verified by simulation or experiment. It is especially suitable for readers who are interested in learning the control scheme of networked systems. This book can benefit researchers, engineers, and students in related fields such as electrical, control, automation, and cyber security.

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.

This book highlights the latest achievements concerning the theory, methods and practice of fault diagnostics, fault tolerant systems and cyber safety. When considering the diagnostics of industrial processes and systems, increasingly important safety issues cannot be ignored. In this context, diagnostics plays a crucial role as a primary measure of the improvement of the overall system safety integrity level. Obtaining the desired diagnostic coverage or providing an appropriate level of inviolability of the integrity of a system is now practically inconceivable without the use of fault detection and isolation methods. Given the breadth and depth of its coverage, the book will be of interest to researchers faced with the challenge of designing technical and medical diagnosis systems, as well as junior researchers and students in the fields of automatic control, robotics, computer science and artificial intelligence.

This book provides a comprehensive overview of wireless technologies for industrial network systems. The authors first describe the concept of industrial network systems and their application to industrial automation. They then go on to cover the role of sensing and control in industrial network systems, and the challenge of sensing and control in the industrial wireless environment. Then, the existing techniques for resource efficiency information transmission are introduced and studied. Afterward, the authors introduce sensing and control-oriented transmission for industrial network systems, which take advantage of spatial diversity gain to overcome the interference and fading, which in turn improves the transmission reliability without expending extra spectrum resources and enlarging the transmission delay. Subsequently, edge assisted efficient transmission schemes are introduced, which integrate the capacities of communication, computing, and control to relieve the contradiction of resource limitation and massive data. Finally, the authors discuss open research issues and future works about information transmission in industrial network systems.

Proceedings of the Tenth Power Systems Computation Conference