Modelling Estimation And Control Of Networked Complex Systems

The paradigm of complexity is pervading both science and engineering, le- ing to the emergence of novel approaches oriented at the development of a systemic view of the phenomena under study; the de?nition of powerful tools for modelling, estimation, and control; and the cross-fertilization of di?erent disciplines and approaches. One of the most promising paradigms to cope with complexity is that of networked systems. Complex, dynamical networks are powerful tools to model, estimate, and control many interesting phenomena, like agent coordination, synch- nization, social and economics events, networks of critical infrastructures, resourcesallocation,informationprocessing,controlovercommunicationn- works, etc. Advances in this ?eld are highlighting approaches that are more and more oftenbasedondynamicalandtime-varyingnetworks,i.e.networksconsisting of dynamical nodes with links that can change over time. Moreover, recent technological advances in wireless communication and decreasing cost and size of electronic devices are promoting the appearance of large inexpensive interconnected systems, each with computational, sensing and mobile ca- bilities. This is fostering the development of many engineering applications, which exploit the availability of these systems of systems to monitor and control very large-scale phenomena with ?ne resolution.

This book presents theoretical and practical findings on the state estimation, diagnosis and control of complex systems, especially in the mathematical form of descriptor systems. The research is fully motivated by real-world applications (i.e., Barcelona’s water distribution network), which require control systems capable of taking into account their specific features and the limits of operations in the presence of uncertainties stemming from modeling errors and component malfunctions. Accordingly, the book first introduces a complete set-based framework for explicitly describing the effects of uncertainties in the descriptor systems discussed. In turn, this set-based framework is used for state estimation and diagnosis. The book also presents a number of application results on economic model predictive control from actual water distribution networks and smart grids. Moreover, the book introduces a fault-tolerant control strategy based on virtual actuators and sensors for such systems in the descriptor form.

The focus of this thesis is the interplay of synchrony and adaptivity in complex networks. Synchronization is a ubiquitous phenomenon observed in different contexts in physics, chemistry, biology, neuroscience, medicine, socioeconomic systems, and engineering. Most prominently, synchronization takes place in the brain, where it is associated with cognitive capacities like learning and memory, but is also a characteristic of neurological diseases like Parkinson and epilepsy. Adaptivity is common in many networks in nature and technology, where the connectivity changes in time, i.e., the strength of the coupling is continuously adjusted depending upon the dynamic state of the system, for instance synaptic neuronal plasticity in the brain. This research contributes to a fundamental understanding of various synchronization patterns, including hierarchical multifrequency clusters, chimeras and other partial synchronization states. After a concise survey of the fundamentals of adaptive and complex dynamical networks and synaptic plasticity, in the first part of the thesis the existence and stability of cluster synchronization in globally coupled adaptive networks is discussed for simple paradigmatic phase oscillators as well as for a more realistic neuronal oscillator model with spike-timing dependent plasticity. In the second part of the thesis the interplay of adaptivity and connectivity is investigated for more complex network structures like nonlocally coupled rings, random networks, and multilayer systems. Besides presenting a plethora of novel, sometimes intriguing patterns of synchrony, the thesis makes a number of pioneering methodological advances, where rigorous mathematical proofs are given in the Appendices. These results are of interest not only from a fundamental point of view, but also with respect to challenging applications in neuroscience and technological systems.

This book presents recent advances in optimization and control methods with applications to industrial engineering and construction management. It consists of 15 chapters authored by recognized experts in a variety of fields including control and operation research, industrial engineering and project management. Topics include numerical methods in unconstrained optimization, robust optimal control problems, set splitting problems, optimum confidence interval analysis, a monitoring networks optimization survey, distributed fault detection, nonferrous industrial optimization approaches, neural networks in traffic flows, economic scheduling of CCHP systems, a project scheduling optimization survey, lean and agile construction project management, practical construction projects in Hong Kong, dynamic project management, production control in PC4P and target contracts optimization. The book offers a valuable reference work for scientists, engineers, researchers and practitioners in industrial engineering and construction management.

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: “Introduction and Background on Control Theory”, “Adaptive Control and Neuroscience”, “Adaptive Learning Algorithms”, “Cyber-Physical Systems and Cooperative Control”, “Applications”. The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists. This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems. • Collection of chapters from several well-known professors and researchers that will showcase their recent work • Presents different state-of-the-art control approaches and theory for complex systems • Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams • Real system examples and figures throughout, make ideas concrete Includes chapters from several well-known professors and researchers that showcases their recent work Presents different state-of-the-art control approaches and theory for complex systems Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems

This book constitutes the refereed proceedings of the 13th International Conference on Systems Simulation, Asia Simulation 2013, held in Singapore, in November 2013. The 45 revised full papers presented together with 18 short papers were carefully reviewed and selected from numerous submissions. The papers address issues such as agent based simulation, scheduling algorithms, simulation methods and tools, simulation and visualization, modeling methodology, simulation in science and engineering, high performance computing and simulation and parallel and distributed simulation.

This book deals with nonlinear dynamics of electronic circuits, which could be used in robot control, secure communications, sensors and synchronized networks. The genesis of the content is related to a course on complex adaptive systems that has been held at the University of Catania since 2005. The efforts are devoted in order to emulate with nonlinear electronic circuits nonlinear dynamics. Step-by-step methods show the essential concepts of complex systems by using the Varela diagrams and accompanying MATLAB® exercises to reinforce new information. Special attention has been devoted to chaotic systems and networks of chaotic circuits by exploring the fundamentals, such as synchronization and control. The aim of the book is to give to readers a comprehensive view of the main concepts of nonlinear dynamics to help them better understand complex systems and their control through the use of electronics devices.

This book sheds new light on the current and future challenges faced by cities, and presents approaches, options and solutions enabled by Information and Communication Technologies (ICT) in the smart city context. By focusing on sustainability objectives within a rapidly changing social, economic, environmental and technological setting, it explores a variety of planning challenges faced by contemporary cities and the power of smart city developments in terms of providing innovative tools, approaches, methodologies and technologies to help cities cope with these challenges. Key issues addressed include smart city (e-) planning and (e-)participation; smart data management to facilitate decision-making processes in cities and insular communities on a variety of topics; smart and sustainable management aspects of climate change, water scarcity, mobility, energy, infrastructure, tourism, blue growth, risk assessment; etc. The book presents current and potential pathways and applications for the evolution of smart cities and communities, taking into consideration the unique problems and opportunities emanating from their specific geographical location. The case study examples mainly concern small and medium-sized cities and communities as well as insular areas in the Mediterranean region, while also incorporating lessons learned from other parts of the world. Their focus is on the specific opportunities and threats emerging in these urban and insular environments, which are characterized by their role as globally known tourist destinations, their coastal or port character, and unique cultural resources, as well as the high rated vulnerability in very many sustainability respects (social, economic, biodiversity, urbanization, migration, poverty, etc.) to be found in the Mediterranean region at large