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.

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.

A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.

We discuss theory and application of extended object tracking. This task is challenging as sensor noise prevents a correct association of the measurements to their sources on the object, the shape itself might be unknown a priori, and due to occlusion effects, only parts of the object are visible at a given time. We propose an approach to track the parameters of arbitrary objects, which provides new solutions to the above challenges, and marks a significant advance to the state of the art.

In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart.

The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.

Extended object tracking deals with estimating the shape and pose of an object based on noisy point measurements. This task is not straightforward, as we may be faced with scarce low-quality measurements, little a priori information, or we may be unable to observe the entire target. This work aims to address these challenges by incorporating ideas from active contours and exploiting information from negative measurements, which tell us where the target cannot be.

The focus of this work is a generic, intraoperative and image-free planning and execution application for arbitrary orthopedic interventions using a novel handheld robotic device and optical see-through glasses (AR). This medical CAD application enables the surgeon to intraoperatively plan the intervention directly on the patient's bone. The glasses and all the other instruments are accurately calibrated using new techniques. Several interventions show the effectiveness of this approach.

This book offers a comprehensive, easy-to-understand overview of receding-horizon control for nonlinear networks. It presents novel general strategies that can simultaneously handle general nonlinear dynamics, system constraints, and disturbances arising in networked and large-scale systems and which can be widely applied. These receding-horizon-control-based strategies can achieve sub-optimal control performance while ensuring closed-loop stability: a feature attractive to engineers. The authors address the problems of networked and distributed control step-by-step, gradually increasing the level of challenge presented. The book first introduces the state-feedback control problems of nonlinear networked systems and then studies output feedback control problems. For large-scale nonlinear systems, disturbance is considered first, then communication delay separately, and lastly the simultaneous combination of delays and disturbances. Each chapter of this easy-to-follow book not only proposes and analyzes novel control algorithms and/or strategies, but also rigorously develops provably correct design conditions. It also provides concise, illustrative examples to demonstrate the implementation procedure, making it invaluable both for academic researchers and engineering practitioners.