Nonlinear and Hybrid Systems in Automotive Control will enable researchers, control engineers and automotive engineers to understand the engine and whole-vehicle models necessary for control. A new generation of control strategies has become necessary because of the increasingly rigorous requirements of vehicle and engine control systems for accuracy, ride comfort, safety, complexity, functionality and emission levels. In contrast with earlier systems, these new control systems are based on dynamic physical models and the principles of advanced nonlinear control. The contributors to this work come from both academic and industrial backgrounds and the subjects they cover include: suspension control; modelling of driver position and behaviour; anti-lock braking systems and optimal braking control; stability analysis of hybrid systems; Hamiltonian formulation of bond graphs; approximation of maximal controlled safe sets for hybrid systems. This book should be of use to academic researchers and graduate students as well as to engineers in the automotive industry.

This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th-16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.

"Information and Control in Networks" demonstrates the way in which system dynamics and information flows intertwine as they evolve, and the central role played by information in the control of complex networked systems. It is a milestone on the road to that convergence from traditionally independent development of control theory and information theory which has emerged strongly in the last fifteen years, and is now a very active research field. In addition to efforts in control and information theory, the text is witness to strong research in such diverse fields as computer science, mathematics, and statistics. Aspects that are given specialist treatment include:

.data-rate theorems;

.computation and control over communication networks;

.decentralized stochastic control;

.Gaussian networks and Gaussian Markov random fields; and

.routability in information networks.""

"Information and Control in Networks" collects contributions from world-leading researchers in the area who came together for the Lund Center for Control of Complex Engineering Systems Workshop in Information and Control in Networks from 17th 19th October 2012; the workshop being the centrepiece of a five-week-long focus period on the same theme. A source of exciting cross-fertilization and new ideas for extensive future research, this volume will be of great interest to any researcher or graduate student interested in the interaction of control and information theory."

Distributed Decision Making and Control is a mathematical treatment of relevant problems in distributed control, decision and multiagent systems, The research reported was prompted by the recent rapid development in large-scale networked and embedded systems and communications. One of the main reasons for the growing complexity in such systems is the dynamics introduced by computation and communication delays. Reliability, predictability, and efficient utilization of processing power and network resources are central issues and the new theory and design methods presented here are needed to analyze and optimize the complex interactions that arise between controllers, plants and networks. The text also helps to meet requirements arising from industrial practice for a more systematic approach to the design of distributed control structures and corresponding information interfaces Theory for coordination of many different control units is closely related to economics and game theory network uses being dictated by congestion-based pricing of a given pathway. The text extends existing methods which represent pricing mechanisms as Lagrange multipliers to distributed optimization in a dynamic setting. In Distributed Decision Making and Control, the main theme is distributed decision making and control with contributions to a general theory and methodology for control of complex engineering systems in engineering, economics and logistics. This includes scalable methods and tools for modeling, analysis and control synthesis, as well as reliable implementations using networked embedded systems. Academic researchers and graduate students in control science, system theory, and mathematical economics and logistics will find mcu to interest them in this collection, first presented orally by the contributors during a sequence of workshops organized in Spring 2010 by the Lund Center for Control of Complex Engineering Systems, a Linnaeus Center at Lund University, Sweden.>

This volume provides a compilation of recent contributions on feedback and robust control, modeling, estimation and filtering. They were presented on the occasion of the sixtieth birthday of Anders Lindquist, who has delivered fundamental contributions to the fields of systems, signals and control for more than three decades. His contributions include seminal work on the role of splitting subspaces in stochastic realization theory, on the partial realization problem for both deterministic and stochastic systems, on the solution of the rational covariance extension problem and on system identification. Lindquist's research includes the development of fast filtering algorithms, leading to a nonlinear dynamical system which computes spectral factors in its steady state, and which provide an alternate, linear in the dimension of the state space, to computing the Kalman gain from a matrix Riccati equation. He established the separation principle for stochastic function differential equations, including some fundamental work on optimal control for stochastic systems with time lags. His recent work on a complete parameterization of all rational solutions to the Nevanlinna-Pick problem is providing a new approach to robust control design.