Download or read book Robust Constraint Satisfaction written by Eric Colin Kerrigan and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Performance and Constraint Satisfaction in Robust Economic Model Predictive Control written by Florian A. Bayer and published by Logos Verlag Berlin GmbH. This book was released on 2017 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we develop a novel framework for model predictive control (MPC) which combines the concepts of robust MPC and economic MPC. The goal of this thesis is to develop and analyze MPC schemes for nonlinear discrete-time systems which explicitly consider the influence of disturbances on arbitrary performance criteria. Instead of regarding the two aspects separately, we propose robust economic MPC approaches that integrate information which is available about the disturbance directly into the economic framework. In more detail, we develop three concepts which differ in which information about the disturbance is used and how this information is taken into account. Furthermore, we provide a thorough theoretical analysis for each of the three approaches. To this end, we present results on the asymptotic average performance as well as on optimal operating regimes. Optimal operating regimes are closely related to the notion of dissipativity, which is therefore analyzed for the presented concepts. Under suitable assumptions, results on necessity and sufficiency of dissipativity for optimal steady-state operation are established for all three robust economic MPC concepts. A detailed discussion is provided which compares the different performance statements derived for the approaches as well as the respective notions of dissipativity.
Download or read book Robust Optimization written by Aharon Ben-Tal and published by Princeton University Press. This book was released on 2009-08-10 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Download or read book An Efficient and Robust Solver for Multi Objective Constraint Satisfaction Problem in Cognitive Radio Systems written by and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Resource Constrained Project Scheduling written by Christian Artigues and published by John Wiley & Sons. This book was released on 2013-03-01 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This title presents a large variety of models and algorithms dedicated to the resource-constrained project scheduling problem (RCPSP), which aims at scheduling at minimal duration a set of activities subject to precedence constraints and limited resource availabilities. In the first part, the standard variant of RCPSP is presented and analyzed as a combinatorial optimization problem. Constraint programming and integer linear programming formulations are given. Relaxations based on these formulations and also on related scheduling problems are presented. Exact methods and heuristics are surveyed. Computational experiments, aiming at providing an empirical insight on the difficulty of the problem, are provided. The second part of the book focuses on several other variants of the RCPSP and on their solution methods. Each variant takes account of real-life characteristics which are not considered in the standard version, such as possible interruptions of activities, production and consumption of resources, cost-based approaches and uncertainty considerations. The last part presents industrial case studies where the RCPSP plays a central part. Applications are presented in various domains such as assembly shop and rolling ingots production scheduling, project management in information technology companies and instruction scheduling for VLIW processor architectures.
Download or read book Principles and Practice of Constraint Programming written by Barry O'Sullivan and published by Springer. This book was released on 2014-08-13 with total page 965 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 20th International Conference on Principles and Practice of Constraint Programming, CP 2014, held in Lyon, France, in September 2014. The 65 revised papers presented together with 4 invited talks were carefully selected from 108 submissions. The scope of CP 2014 includes all aspects of computing with constraints, including theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource allocation, and agreement technologies.
Download or read book Robust and Online Large Scale Optimization written by Ravindra K. Ahuja and published by Springer. This book was released on 2009-10-21 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scheduled transportation networks give rise to very complex and large-scale networkoptimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. Examples of scheduled transportation include bus, ferry, airline, and railway networks, with the latter being a prime application domain that provides a fair amount of the most complex and largest instances of such optimization problems. Scheduled transport optimization deals with planning and scheduling problems over several time horizons, and substantial progress has been made for strategic planning and scheduling problems in all transportation domains. This state-of-the-art survey presents the outcome of an open call for contributions asking for either research papers or state-of-the-art survey articles. We received 24 submissions that underwent two rounds of the standard peer-review process, out of which 18 were finally accepted for publication. The volume is organized in four parts: Robustness and Recoverability, Robust Timetabling and Route Planning, Robust Planning Under Scarce Resources, and Online Planning: Delay and Disruption Management.
Download or read book Principles and Practice of Constraint Programming CP 2005 written by Peter van Beek and published by Springer. This book was released on 2005-10-19 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 11th International Conference on the Principles and Practice of Constraint Programming (CP 2005) was held in Sitges (Barcelona), Spain, October 1-5, 2005. Information about the conference can be found on the web at http://www.iiia.csic.es/cp2005/.Informationaboutpastconferencesinthe series can be found athttp://www.cs.ualberta.ca/~ai/cp/. The CP conference series is the premier international conference on c- straint programming and is held annually. The conference is concerned with all aspects of computing with constraints, including: algorithms, applications, environments, languages, models and systems. This year, we received 164 submissions. All of the submitted papers received atleastthreereviews, andthepapersandtheirreviewswerethenextensivelyd- cussed during an online Program Committee meeting. As a result, the Program Committee chose 48 (29.3%) papers to be published in full in the proceedings and a further 22 (13.4%)papers to be published as short papers.The full papers werepresentedattheconferencein twoparalleltracksandtheshortpaperswere presented as posters during a lively evening session. Two papers were selected by a subcommittee of the ProgramCommittee--consisting of Chris Beck, Gilles Pesant, and myself--to receive best paper awards. The conference program also includedexcellentinvitedtalksbyHþ ectorGe?ner, IanHorrocks, FrancescaRossi, and Peter J. Stuckey. As a permanent record, the proceedings contain four-page extended abstracts of the invited talks.
Download or read book Principles and Practice of Constraint Programming written by J. Christopher Beck and published by Springer. This book was released on 2017-08-22 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed conference proceedings of the 23nd International Conference on Principles and Practice of Constraint Programming, CP 2017, held in Melbourne, Australia from August 28, 2017 until September 1, 2017. The conference is colocated with the 20th International Conference on Theory and Applications of Satisfiability Testing (SAT 2017) and the 33rd International Conference on Logic Programming. The 46 revised full papers presented were carefully reviewed and selected from 115 submissions. The scope of the contributions includes all aspects of computing with constraints, including theory, algorithms, environments, languages, models, systems, and applications such as decision making, resource al location, scheduling, configuration, and planning. The papers are grouped into the following tracks: technical track; application track; machine learning & CP track; operations research & CP track; satisfiability & CP track, test and verification & CP track; journal & sister conference track.
Download or read book Minimax Approaches to Robust Model Predictive Control written by Johan Löfberg and published by Linköping University Electronic Press. This book was released on 2003-04-11 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Controlling a system with control and state constraints is one of the most important problems in control theory, but also one of the most challenging. Another important but just as demanding topic is robustness against uncertainties in a controlled system. One of the most successful approaches, both in theory and practice, to control constrained systems is model predictive control (MPC). The basic idea in MPC is to repeatedly solve optimization problems on-line to find an optimal input to the controlled system. In recent years, much effort has been spent to incorporate the robustness problem into this framework. The main part of the thesis revolves around minimax formulations of MPC for uncertain constrained linear discrete-time systems. A minimax strategy in MPC means that worst-case performance with respect to uncertainties is optimized. Unfortunately, many minimax MPC formulations yield intractable optimization problems with exponential complexity. Minimax algorithms for a number of uncertainty models are derived in the thesis. These include systems with bounded external additive disturbances, systems with uncertain gain, and systems described with linear fractional transformations. The central theme in the different algorithms is semidefinite relaxations. This means that the minimax problems are written as uncertain semidefinite programs, and then conservatively approximated using robust optimization theory. The result is an optimization problem with polynomial complexity. The use of semidefinite relaxations enables a framework that allows extensions of the basic algorithms, such as joint minimax control and estimation, and approx- imation of closed-loop minimax MPC using a convex programming framework. Additional topics include development of an efficient optimization algorithm to solve the resulting semidefinite programs and connections between deterministic minimax MPC and stochastic risk-sensitive control. The remaining part of the thesis is devoted to stability issues in MPC for continuous-time nonlinear unconstrained systems. While stability of MPC for un-constrained linear systems essentially is solved with the linear quadratic controller, no such simple solution exists in the nonlinear case. It is shown how tools from modern nonlinear control theory can be used to synthesize finite horizon MPC controllers with guaranteed stability, and more importantly, how some of the tech- nical assumptions in the literature can be dispensed with by using a slightly more complex controller.
Download or read book Efficient Algorithms for Strong Local Consistencies and Adaptive Techniques in Constraint Satisfaction Problems written by Anastasia Paparrizou and published by Lulu.com. This book was released on 2015-03-23 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: Constraint programming is a successful technology for solving a wide range of problems in business and industry which require satisfying a set of constraints. Central to solving constraint satisfaction problems is enforcing a level of local consistency. In this thesis, we propose efficient filtering algorithms for enforcing strong local consistencies. In addition, since such filtering algorithms can be too expensive to enforce all the time, we propose some automated heuristics that can dynamically select the most appropriate filtering algorithm. Published by AI Access, a not-for-profit publisher of open access texts with a highly respected scientific board. We publish monographs and collected works. Our texts are available electronically for free and in hard copy at close to cost.
Download or read book Global Optimization and Constraint Satisfaction written by COCOS 200 and published by Springer Science & Business Media. This book was released on 2003-11-05 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Global Constraints Optimization and Costraint Satisfaction, COCOS 2002, held in Valbonne-Sophia Antipolis, France in October 2002. The 15 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement. The papers address current issues in global optimization, mathematical programming, and constraint programming; they are grouped in topical sections on optimization, constraint satisfaction, and benchmarking.
Download or read book Set Theoretic Methods in Control written by Franco Blanchini and published by Birkhäuser. This book was released on 2015-07-02 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this monograph describes the set-theoretic approach for the control and analysis of dynamic systems, both from a theoretical and practical standpoint. This approach is linked to fundamental control problems, such as Lyapunov stability analysis and stabilization, optimal control, control under constraints, persistent disturbance rejection, and uncertain systems analysis and synthesis. Completely self-contained, this book provides a solid foundation of mathematical techniques and applications, extensive references to the relevant literature, and numerous avenues for further theoretical study. All the material from the first edition has been updated to reflect the most recent developments in the field, and a new chapter on switching systems has been added. Each chapter contains examples, case studies, and exercises to allow for a better understanding of theoretical concepts by practical application. The mathematical language is kept to the minimum level necessary for the adequate formulation and statement of the main concepts, yet allowing for a detailed exposition of the numerical algorithms for the solution of the proposed problems. Set-Theoretic Methods in Control will appeal to both researchers and practitioners in control engineering and applied mathematics. It is also well-suited as a textbook for graduate students in these areas. Praise for the First Edition "This is an excellent book, full of new ideas and collecting a lot of diverse material related to set-theoretic methods. It can be recommended to a wide control community audience." - B. T. Polyak, Mathematical Reviews "This book is an outstanding monograph of a recent research trend in control. It reflects the vast experience of the authors as well as their noticeable contributions to the development of this field...[It] is highly recommended to PhD students and researchers working in control engineering or applied mathematics. The material can also be used for graduate courses in these areas." - Octavian Pastravanu, Zentralblatt MATH
Download or read book Foundations of Constraint Satisfaction written by Edward Tsang and published by BoD – Books on Demand. This book was released on 2014-05-13 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This seminal text of Computer Science, the most cited book on the subject, is now available for the first time in paperback. Constraint satisfaction is a decision problem that involves finite choices. It is ubiquitous. The goal is to find values for a set of variables that will satisfy a given set of constraints. It is the core of many applications in artificial intelligence, and has found its application in many areas, such as planning and scheduling. Because of its generality, most AI researchers should be able to benefit from having good knowledge of techniques in this field. Originally published in 1993, this now classic book was the first attempt to define the scope of constraint satisfaction. It covers both the theoretical and the implementation aspects of the subject. It provides a framework for studying this field, relates different research, and resolves ambiguity in a number of concepts and algorithms in the literature. This seminal text is arguably the most rigorous book in the field. All major concepts were defined in First Order Predicate Calculus. Concepts defined this way are precise and unambiguous.
Download or read book Robust Model Predictive Control for Large Scale Manufacturing Systems subject to Uncertainties written by Jens Tonne and published by kassel university press GmbH. This book was released on 2018-01-19 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large scale manufacturing systems are often run with constant process parameters although continuous and abrupt disturbances influence the process. To reduce quality variations and scrap, a closed-loop control of the process variables becomes indispensable. In this thesis, a modeling and control framework for multistage manufacturing systems is developed, in which the systems are subject to abrupt faults, such as component defects, and continuous disturbances. In this context, three main topics are considered: the development of a modeling framework, the design of robust distributed controllers, and the application of both to the models of a real hot stamping line. The focus of all topics is on the control of the product properties considering the available knowledge of faults and disturbances.
Download or read book Model Predictive Control written by Basil Kouvaritakis and published by Springer. This book was released on 2015-12-01 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplicative and stochastic model uncertainty. The book provides: extensive use of illustrative examples; sample problems; and discussion of novel control applications such as resource allocation for sustainable development and turbine-blade control for maximized power capture with simultaneously reduced risk of turbulence-induced damage. Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject. For the instructor it provides an authoritative resource for the construction of courses.
Download or read book Chance Constrained Optimization with Robust and Sampling Approximation written by Zhuangzhi Li and published by . This book was released on 2015 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty is pervasive in various process operations problems. Its appearance spans from the detailed process description of multi-site manufacturing. In practical applications, there is a need for making optimal and reliable decisions in the presence of uncertainties. Asking for constraint satisfaction at some level of probability is reasonable in many applications, which calls for the utilization of chance constraints. This thesis studies the approximation methods for solving the chance constrained optimization problems. Two approximation methods were considered: Robust optimization approximation and Sample average approximation. For the robust optimization approximation method, an optimal uncertainty set size identification algorithm is proposed, which can find the smallest possible uncertainty set size that leads to the least conservative robust optimization approximation. For the sample average approximation method, a new linear programming based scenario reduction method is proposed, which can reduce the number of samples used in the sample average approximation problem, thus lead to reduction of computational complexity. Furthermore, the proposed scenario reduction method is computationally more efficient than the existing methods. The effectiveness of the proposed methods is demonstrated by several case studies.
