The book gives an introduction to networked control systems and describes new modeling paradigms, analysis methods for event-driven, digitally networked systems, and design methods for distributed estimation and control. Networked model predictive control is developed as a means to tolerate time delays and packet loss brought about by the communication network. In event-based control the traditional periodic sampling is replaced by state-dependent triggering schemes. Novel methods for multi-agent systems ensure complete or clustered synchrony of agents with identical or with individual dynamics. The book includes numerous references to the most recent literature. Many methods are illustrated by numerical examples or experimental results.

This thesis considers networked discrete-event systems. The overall system is a network of subsystems, each of which includes a technical process modelled by an I/O automaton together with a controller and a network unit. These subsystems are interconnected by physical couplings and digital communication links. An important characteristic of the networked discreteevent systems is the partial autonomy of the subsystems, which is reflected by the fact that each subsystem solves its local tasks individually. Cooperation among the subsystems becomes necessary if physical couplings or control specifications have to be resolved by two or more subsystems in order to satisfy the local tasks. Hence, the subsystems participate in satisfying cooperative tasks by adapting their behaviours while using the communication network without a coordinator. In these situations the following question arises: When and what information has to be exchanged by the subsystems and what should the structure of the communication network look like? As a main result of this thesis, it is proved that the subsystems in the networked discrete-event system determine deadlock-free execution orders of cooperative tasks with distributed model information by using the communication network and solving their local tasks. The applicability of the cooperative control solution is demonstrated by means of a collaborative process at the Handling System HANS. Markus Zgorzelski received his Bachelor in Electrical Engineering and Information Science from the Ruhr-Universität Bochum in 2011 and he received his Masters in Electrical Engineering and Information Science from the Ruhr-Universität Bochum in 2014. From 2014 to 2020 he was a scientific co-worker at the Institute of Automation and Computer Control, where he obtained his PhD. His research was focused on networked discrete-event systems.

This thesis presents a novel distributed control paradigm for networked control systems in which the local control units of the subsystems exchange information, whenever this is necessary to fulfill an overall control aim. The local control units act in a self-organized way, which means that they adapt their communication structure depending on the current situation of the subsystems based on locally available information only. A new controller structure is proposed. The local control units are divided into three components fulfilling universal tasks to generate a situation-dependent communication structure: The feedback unit performs a local feedback by using local measurements to fulfill basic performance requirements. The observation unit detects the current situation of the subsystem by evaluating locally available information. The decision unit decides about the transmission of information from the corresponding subsystem to other local control units. Two self-organizing controllers for physically interconnected systems in which the local control units adapt the communication among each other depending on the current disturbances are introduced. Furthermore, three novel self-organizing controllers for synchronizing multi-agent systems within leader-follower structures by adapting the communication structure to situations like set-point changes, disturbances and communication faults are proposed. The concepts are applied in order to control a water supply system and a robot formation.

The problem of jointly designing a robust controller and an intelligent scheduler for networked control systems (NCSs) is addressed in this thesis. NCSs composing of multiple plants that share a single channel communication network with uncertain time-varying transmission times are modeled as switched polytopic systems with additive norm-bounded uncertainty. Switching is deployed to represent scheduling, the polytopic uncertainty to overapproximatively describe the uncertain time-varying transmission times. Based on the resulting NCS model and a state feedback control law, the control and scheduling codesign problem is then introduced and formulated as a robust (minimax) optimization problem with the objective of minimizing the worst-case value of an associated infinite time-horizon quadratic cost function. Five robust codesign strategies are investigated for tackling the introduced optimization problem, namely: Periodic control and scheduling (PCS), Receding-horizon control and scheduling (RHCS), Implementation-aware control and scheduling (IACS), Event-based control and scheduling (EBCS), Prediction-based control and scheduling (PBCS). All these codesign strategies are determined from LMI optimization problems based on the Lyapunov theory. The properties of each are evaluated and compared in terms of computational complexity and control performance based on simulation and experimental study, showing their effectiveness in improving the performance while utilizing the limited communication resources very efficiently.

In Networked Control Systems (NCS), components of a control loop are connected by data networks that may introduce time-varying delays and packet losses into the system, which can severly degrade control performance. Hence, this book presents the newly developed S-LQG (Sequence-Based Linear Quadratic Gaussian) controller that combines the sequence-based control method with the well-known LQG approach to stochastic optimal control in order to compensate for the network-induced effects.

Cyber-physical systems are the next step in realizing the centuries old ubiquitous computing idea by focusing on open real-time systems design and device connectivity. Mobile ad hoc networks offer the flexible, local connectivity that cyber-physical systems require, if the connectivity can be realized dependably. One aspect of the dependability is the prediction of connectivity in the mobile ad hoc network. The presented research contributes to the connectivity prediction in mobile ad hoc networks with moving network participants in two ways: It systematically analyses the influence of scenario parameters on a set of connectivity metrics and it proposes and evaluates three classes of prediction models for these metrics.

Cyber-physical systems play a crucial role in connecting aspects of online life to physical life. By studying emerging trends in these systems, programming techniques can be optimized and strengthened to create a higher level of effectiveness. Solutions for Cyber-Physical Systems Ubiquity is a critical reference source that discusses the issues and challenges facing the implementation, usage, and challenges of cyber-physical systems. Highlighting relevant topics such as the Internet of Things, smart-card security, multi-core environments, and wireless sensor nodes, this scholarly publication is ideal for engineers, academicians, computer science students, and researchers that would like to stay abreast of current methodologies and trends involving cyber-physical system progression.

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.