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Author : Jinfeng Liu,Helen E Durand
Publisher : MDPI
Page : 231 pages
File Size : 42,8 Mb
Release : 2019-01-16
Category : Engineering (General). Civil engineering (General)
ISBN : 9783038974208
This book is a printed edition of the Special Issue "New Directions on Model Predictive Control" that was published in Mathematics
Author : Jinfeng Liu,Helen E Durand
Publisher : Unknown
Page : 1 pages
File Size : 42,8 Mb
Release : 2019
Category : Electronic books
ISBN : 3038974218
Model predictive control (MPC) is an advanced control design used in many industries worldwide. An MPC selects control actions which are optimal with respect to a given performance metric as well as any physically-motivated constraints. MPC has therefore gained significant research attention over the past several decades. Advances in MPC continue to unlock its potential to solve a wide variety of practical issues. This book presents some of the state-of-the-art in MPC design from theoretical and applications perspectives. It covers a broad spectrum of MPC application areas, reviewing applications as diverse as air conditioning, pharmaceutical manufacturing, mineral column flotation, actuator faults, and hydraulic fracturing, while also highlighting recent theoretical advancements in control technology that integrate it with data-driven models, zone tracking, or process safety and cybersecurity. Both centralized and distributed MPC formulations are presented. The purpose of this book is to assemble a collection of current research in MPC that handles practically-motivated theoretical issues as well as recent MPC applications, with the aim of highlighting the significant potential benefits of new MPC theory and design.
Author : Rolf Findeisen,Frank Allgöwer,Lorenz Biegler
Publisher : Springer
Page : 644 pages
File Size : 48,7 Mb
Release : 2007-09-08
Category : Technology & Engineering
ISBN : 9783540726999
Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.
Author : Michael A. Boudreau,Gregory K. McMillan
Publisher : ISA
Page : 356 pages
File Size : 44,5 Mb
Release : 2007
Category : Science
ISBN : 1556179057
Models offer benefits even before they are put on line. Based on years of experience, the authors reveal in New Directions in Bioprocess Modeling and Control that significant improvements can result from the process knowledge and insight that are gained when building experimental and first-principle models for process monitoring and control. Doing modeling in the process development and early commercialization phases is advantageous because it increases process efficiency and provides ongoing opportunities for improving process control. This technology is important for maximizing benefits from analyzers and control tool investments. If you are a process design, quality control, information systems, or automation engineer in the biopharmaceutical, brewing, or bio-fuel industry, this handy resource will help you define, develop, and apply a virtual plant, model predictive control, first-principle models, neural networks, and multivariate statistical process control. The synergistic knowledge discovery on bench top or pilot plant scale can be ported to industrial scale processes. This learning process is consistent with the intent in the Process Analyzer and Process Control Tools sections of the FDA_s Guidance for Industry PAT _ A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. It states in the Process Analyzer section of the FDA_s guidance: _For certain applications, sensor-based measurements can provide a useful process signature that may be related to the underlying process steps or transformations. Based on the level of process understanding these signatures may also be useful for the process monitoring, control, and end point determination when these patterns or signatures relate to product and process quality._
Author : Frank Allgöwer,Alex Zheng
Publisher : Birkhäuser
Page : 463 pages
File Size : 49,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783034884075
During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
Author : Eduardo F. Camacho,Carlos Bordons Alba
Publisher : Springer Science & Business Media
Page : 405 pages
File Size : 50,9 Mb
Release : 2013-01-10
Category : Technology & Engineering
ISBN : 9780857293985
The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. The book demonstrates that a powerful technique does not always require complex control algorithms. Many new exercises and examples have also been added throughout. Solutions available for download from the authors' website save the tutor time and enable the student to follow results more closely even when the tutor isn't present.
Author : Lalo Magni,Davide Martino Raimondo,Frank Allgöwer
Publisher : Springer Science & Business Media
Page : 562 pages
File Size : 50,7 Mb
Release : 2009-05-25
Category : Technology & Engineering
ISBN : 9783642010934
Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. More than 250 papers have been published in 2006 in ISI Journals. With this book we want to bring together the contributions of a diverse group of internationally well recognized researchers and industrial practitioners, to critically assess the current status of the NMPC field and to discuss future directions and needs. The book consists of selected papers presented at the International Workshop on Assessment an Future Directions of Nonlinear Model Predictive Control that took place from September 5 to 9, 2008, in Pavia, Italy.
Author : Matthew Ellis,Jinfeng Liu,Panagiotis D. Christofides
Publisher : Springer
Page : 311 pages
File Size : 48,5 Mb
Release : 2016-07-27
Category : Technology & Engineering
ISBN : 9783319411088
This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes.In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.
Author : Bruno Apolloni,Simone Bassis,M. Marinaro
Publisher : IOS Press
Page : 276 pages
File Size : 55,9 Mb
Release : 2009
Category : Computers
ISBN : 9781586039844
A collection of selected papers from the 18th WIRN workshop, the annual meeting of the Italian Neural Networks Society (SIREN). It is divided in two general subjects, 'models' and 'applications' and two specific ones, 'economy and complexity' and 'remote sensing image processing'.
Author : Alexandra Grancharova,Tor Arne Johansen
Publisher : Springer Science & Business Media
Page : 241 pages
File Size : 45,6 Mb
Release : 2012-03-23
Category : Technology & Engineering
ISBN : 9783642287794
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: ؠ Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; - Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs; - Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty); - Nonlinear systems, consisting of interconnected nonlinear sub-systems. The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.
Author : José M. Maestre,Rudy R. Negenborn
Publisher : Springer Science & Business Media
Page : 601 pages
File Size : 42,7 Mb
Release : 2013-11-10
Category : Technology & Engineering
ISBN : 9789400770065
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.
Author : Carlos Bordons Alba
Publisher : Springer Science & Business Media
Page : 290 pages
File Size : 40,9 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781447133988
The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. The book demonstrates that a powerful technique does not always require complex control algorithms. Many new exercises and examples have also been added throughout. Solutions available for download from the authors' website save the tutor time and enable the student to follow results more closely even when the tutor isn't present.
Author : Lars Grüne,Jürgen Pannek
Publisher : Springer
Page : 456 pages
File Size : 50,7 Mb
Release : 2016-11-09
Category : Technology & Engineering
ISBN : 9783319460246
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. 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. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including: • a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium; • a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems; • an extended discussion of stability and performance using approximate updates rather than full optimization; • replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and • further variations and extensions in response to suggestions from readers of the first edition. Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.
Author : Carlos Bordons,Félix Garcia-Torres,Miguel A. Ridao
Publisher : Springer Nature
Page : 266 pages
File Size : 55,9 Mb
Release : 2019-09-12
Category : Technology & Engineering
ISBN : 9783030245702
The book shows how the operation of renewable-energy microgrids can be facilitated by the use of model predictive control (MPC). It gives readers a wide overview of control methods for microgrid operation at all levels, ranging from quality of service, to integration in the electricity market. MPC-based solutions are provided for the main control issues related to energy management and optimal operation of microgrids. The authors present MPC techniques for case studies that include different renewable sources – mainly photovoltaic and wind – as well as hybrid storage using batteries, hydrogen and supercapacitors. Experimental results for a pilot-scale microgrid are also presented, as well as simulations of scheduling in the electricity market and integration of electric and hybrid vehicles into the microgrid. in order to replicate the examples provided in the book and to develop and validate control algorithms on existing or projected microgrids. Model Predictive Control of Microgrids will interest researchers and practitioners, enabling them to keep abreast of a rapidly developing field. The text will also help to guide graduate students through processes from the conception and initial design of a microgrid through its implementation to the optimization of microgrid management. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
Author : Tao Zheng
Publisher : BoD – Books on Demand
Page : 434 pages
File Size : 49,6 Mb
Release : 2011-07-05
Category : Technology & Engineering
ISBN : 9789533072982
Model Predictive Control (MPC) refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance. From lower request of modeling accuracy and robustness to complicated process plants, MPC has been widely accepted in many practical fields. As the guide for researchers and engineers all over the world concerned with the latest developments of MPC, the purpose of "Advanced Model Predictive Control" is to show the readers the recent achievements in this area. The first part of this exciting book will help you comprehend the frontiers in theoretical research of MPC, such as Fast MPC, Nonlinear MPC, Distributed MPC, Multi-Dimensional MPC and Fuzzy-Neural MPC. In the second part, several excellent applications of MPC in modern industry are proposed and efficient commercial software for MPC is introduced. Because of its special industrial origin, we believe that MPC will remain energetic in the future.
