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This text provides an approach to the study of perturbation and discretization effects on the long-time behaviour of dynamical and control systems. It analyzes the impact of time and space discretizations on asymptotically stable attracting sets, attractors and asumptotically controllable sets.
This book provides an approach to the study of perturbation and discretization effects on the long-time behavior of dynamical and control systems. It analyzes the impact of time and space discretizations on asymptotically stable attracting sets, attractors, asumptotically controllable sets and their respective domains of attractions and reachable sets. Combining robust stability concepts from nonlinear control theory, techniques from optimal control and differential games and methods from nonsmooth analysis, both qualitative and quantitative results are obtained and new algorithms are developed, analyzed and illustrated by examples.
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.
This new text/reference is an excellent resource for the foundations and applications of control theory and nonlinear dynamics. All graduates, practitioners, and professionals in control theory, dynamical systems, perturbation theory, engineering, physics and nonlinear dynamics will find the book a rich source of ideas, methods and applications. With its careful use of examples and detailed development, it is suitable for use as a self-study/reference guide for all scientists and engineers.
Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.
Recently, the public attention has turned toward the intricate interrelation between economic growth and global warming. This book focuses on this nexus but broadens the framework to study the issue. Growth is seen as global growth, which affects the global environment and climate change. Global growth, in particular high economic growth rates, imply a fast depletion of renewable and non-renewable resources. Thus this book deals with the impact of the environment and the effect of the exhaustive use of natural resources on economic growth and welfare of market economies as well as the reverse linkage. It is arranged in three parts: Part I of the book discusses the environment and growth. The...
Mathematical optimization encompasses both a rich and rapidly evolving body of fundamental theory, and a variety of exciting applications in science and engineering. The present book contains a careful selection of articles on recent advances in optimization theory, numerical methods, and their applications in engineering. It features in particular new methods and applications in the fields of optimal control, PDE-constrained optimization, nonlinear optimization, and convex optimization. The authors of this volume took part in the 14th Belgian-French-German Conference on Optimization (BFG09) organized in Leuven, Belgium, on September 14-18, 2009. The volume contains a selection of reviewed articles contributed by the conference speakers as well as three survey articles by plenary speakers and two papers authored by the winners of the best talk and best poster prizes awarded at BFG09. Researchers and graduate students in applied mathematics, computer science, and many branches of engineering will find in this book an interesting and useful collection of recent ideas on the methods and applications of optimization.
Given the industrialized world’s historical dependence on fossil fuel-based energy resources and the now-realized perils of moving beyond the earth’s carbon budget, this book explores the myriad challenges of climate change and in reaching a low-carbon economy. Reconciling the medium-term competing, yet frequently complementary, needs for transition policies, the book provides guidelines for complex and often conflicting climate policy tasks. The book presents empirical trends in the use of carbon-emitting resources and evaluates market-driven short-termism and its adverse impact on resource use and the environment; it emphasizes a medium-term macroeconomic perspective for the transition...
Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “...
The major goal of the book is to create an environment for matching different d- ciplinary approaches to studying economic growth. This goal is implemented on the basis of results of the Symposium “Applications of Dynamic Systems to E- nomic Growth with Environment” which was held at the International Institute for Applied Systems Analysis (IIASA) on the 7th–8th of November, 2008, within the IIASA Project “Driving Forces of Economic Growth” (ECG). The symposium was organized by coordinators of the ECG project: Jesus Crespo-Cuaresma from IIASA World Population Program, and Tapio Palokangas and Alexander Tarasyev from IIASA Dynamic Systems Program. The book addresses the issues of su...