Explicit Nonlinear Model Predictive Control

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Explicit Nonlinear Model Predictive Control

Alexandra Grancharova,Tor Arne Johansen
Explicit Nonlinear Model Predictive Control Book PDF

Author : Alexandra Grancharova,Tor Arne Johansen
Publisher : Springer
Page : 241 pages
File Size : 40,8 Mb
Release : 2012-03-22
Category : Technology & Engineering
ISBN : 9783642287800

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Explicit Nonlinear Model Predictive Control by Alexandra Grancharova,Tor Arne Johansen Pdf

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.

Assessment and Future Directions of Nonlinear Model Predictive Control

Rolf Findeisen,Frank Allgöwer,Lorenz Biegler
Assessment and Future Directions of Nonlinear Model Predictive Control Book PDF

Author : Rolf Findeisen,Frank Allgöwer,Lorenz Biegler
Publisher : Springer
Page : 644 pages
File Size : 42,5 Mb
Release : 2007-09-08
Category : Technology & Engineering
ISBN : 9783540726999

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Assessment and Future Directions of Nonlinear Model Predictive Control by Rolf Findeisen,Frank Allgöwer,Lorenz Biegler Pdf

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.

Nonlinear Model Predictive Control

Lalo Magni,Davide Martino Raimondo,Frank Allgöwer
Nonlinear Model Predictive Control Book PDF

Author : Lalo Magni,Davide Martino Raimondo,Frank Allgöwer
Publisher : Springer Science & Business Media
Page : 562 pages
File Size : 51,7 Mb
Release : 2009-05-25
Category : Technology & Engineering
ISBN : 9783642010934

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Nonlinear Model Predictive Control by Lalo Magni,Davide Martino Raimondo,Frank Allgöwer Pdf

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.

Nonlinear Model Predictive Control

Frank Allgöwer,Alex Zheng
Nonlinear Model Predictive Control Book PDF

Author : Frank Allgöwer,Alex Zheng
Publisher : Birkhäuser
Page : 463 pages
File Size : 53,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9783034884075

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Nonlinear Model Predictive Control by Frank Allgöwer,Alex Zheng Pdf

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.

Handbook of Model Predictive Control

Saša V. Raković,William S. Levine
Handbook of Model Predictive Control Book PDF

Author : Saša V. Raković,William S. Levine
Publisher : Springer
Page : 692 pages
File Size : 42,9 Mb
Release : 2018-09-01
Category : Science
ISBN : 9783319774893

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Handbook of Model Predictive Control by Saša V. Raković,William S. Levine Pdf

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 “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.

Model Predictive Control

Ridong Zhang,Anke Xue,Furong Gao
Model Predictive Control Book PDF

Author : Ridong Zhang,Anke Xue,Furong Gao
Publisher : Springer
Page : 137 pages
File Size : 42,9 Mb
Release : 2018-08-14
Category : Technology & Engineering
ISBN : 9789811300837

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Model Predictive Control by Ridong Zhang,Anke Xue,Furong Gao Pdf

This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of process system engineering and control engineering.

Predictive Control for Linear and Hybrid Systems

Francesco Borrelli,Alberto Bemporad,Manfred Morari
Predictive Control for Linear and Hybrid Systems Book PDF

Author : Francesco Borrelli,Alberto Bemporad,Manfred Morari
Publisher : Cambridge University Press
Page : 447 pages
File Size : 45,7 Mb
Release : 2017-06-22
Category : Mathematics
ISBN : 9781107016880

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Predictive Control for Linear and Hybrid Systems by Francesco Borrelli,Alberto Bemporad,Manfred Morari Pdf

With a simple approach that includes real-time applications and algorithms, this book covers the theory of model predictive control (MPC).

Modelling and Control of Dynamic Systems Using Gaussian Process Models

Juš Kocijan
Modelling and Control of Dynamic Systems Using Gaussian Process Models Book PDF

Author : Juš Kocijan
Publisher : Springer
Page : 267 pages
File Size : 54,8 Mb
Release : 2015-11-21
Category : Technology & Engineering
ISBN : 9783319210216

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Modelling and Control of Dynamic Systems Using Gaussian Process Models by Juš Kocijan Pdf

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Nonlinear Model Predictive Control of Combustion Engines

Thivaharan Albin Rajasingham
Nonlinear Model Predictive Control of Combustion Engines Book PDF

Author : Thivaharan Albin Rajasingham
Publisher : Springer Nature
Page : 330 pages
File Size : 48,7 Mb
Release : 2021-04-27
Category : Technology & Engineering
ISBN : 9783030680107

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Nonlinear Model Predictive Control of Combustion Engines by Thivaharan Albin Rajasingham Pdf

This book provides an overview of the nonlinear model predictive control (NMPC) concept for application to innovative combustion engines. Readers can use this book to become more expert in advanced combustion engine control and to develop and implement their own NMPC algorithms to solve challenging control tasks in the field. The significance of the advantages and relevancy for practice is demonstrated by real-world engine and vehicle application examples. The author provides an overview of fundamental engine control systems, and addresses emerging control problems, showing how they can be solved with NMPC. The implementation of NMPC involves various development steps, including: • reduced-order modeling of the process; • analysis of system dynamics; • formulation of the optimization problem; and • real-time feasible numerical solution of the optimization problem. Readers will see the entire process of these steps, from the fundamentals to several innovative applications. The application examples highlight the actual difficulties and advantages when implementing NMPC for engine control applications. Nonlinear Model Predictive Control of Combustion Engines targets engineers and researchers in academia and industry working in the field of engine control. The book is laid out in a structured and easy-to-read manner, supported by code examples in MATLAB®/Simulink®, thus expanding its readership to students and academics who would like to understand the fundamental concepts of NMPC. 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.

Model Predictive Control for Nonlinear Continuous-Time Systems with and Without Time-Delays

Marcus Reble
Model Predictive Control for Nonlinear Continuous Time Systems with and Without Time Delays Book PDF

Author : Marcus Reble
Publisher : Logos Verlag Berlin GmbH
Page : 159 pages
File Size : 46,7 Mb
Release : 2013
Category : Mathematics
ISBN : 9783832533816

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Model Predictive Control for Nonlinear Continuous-Time Systems with and Without Time-Delays by Marcus Reble Pdf

The objective of this thesis is the development of novel model predictive control (MPC) schemes for nonlinear continuous-time systems with and without time-delays in the states which guarantee asymptotic stability of the closed-loop. The most well-studied MPC approaches with guaranteed stability use a control Lyapunov function as terminal cost. Since the actual calculation of such a function can be difficult, it is desirable to replace this assumption by a less restrictive controllability assumption. For discrete-time systems, the latter assumption has been used in the literature for the stability analysis of so-called unconstrained MPC, i.e., MPC without terminal cost and terminal constraints. The contributions of this thesis are twofold. In the first part, we propose novel MPC schemes with guaranteed stability based on a controllability assumption, whereas we extend different MPC schemes with guaranteed stability to nonlinear time-delay systems in the second part. In the first part of this thesis, we derive explicit stability conditions on the prediction horizon as well as performance guarantees for unconstrained MPC. Starting from this result, we propose novel alternative MPC formulations based on combinations of the controllability assumption with terminal cost and terminal constraints. One of the main contributions is the development of a unifying MPC framework which allows to consider both MPC schemes with terminal cost and terminal constraints as well as unconstrained MPC as limit cases of our framework. In the second part of this thesis, we show that several MPC schemes with and without terminal constraints can be extended to nonlinear time-delay systems. Due to the infinite-dimensional nature of these systems, the problem is more involved and additional assumptions are required in the controller design. We investigate different MPC schemes with and without terminal constraints and/or terminal cost terms and derive novel stability conditions. Furthermore, we pay particular attention to the calculation of the involved control design parameters.

Encyclopedia of Systems and Control

John Baillieul,Tariq Samad
Encyclopedia of Systems and Control Book PDF

Author : John Baillieul,Tariq Samad
Publisher : Springer
Page : 1554 pages
File Size : 44,9 Mb
Release : 2015-07-29
Category : Technology & Engineering
ISBN : 1447150570

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Encyclopedia of Systems and Control by John Baillieul,Tariq Samad Pdf

The Encyclopedia of Systems and Control collects a broad range of short expository articles that describe the current state of the art in the central topics of control and systems engineering as well as in many of the related fields in which control is an enabling technology. The editors have assembled the most comprehensive reference possible, and this has been greatly facilitated by the publisher’s commitment continuously to publish updates to the articles as they become available in the future. Although control engineering is now a mature discipline, it remains an area in which there is a great deal of research activity, and as new developments in both theory and applications become available, they will be included in the online version of the encyclopedia. A carefully chosen team of leading authorities in the field has written the well over 250 articles that comprise the work. The topics range from basic principles of feedback in servomechanisms to advanced topics such as the control of Boolean networks and evolutionary game theory. Because the content has been selected to reflect both foundational importance as well as subjects that are of current interest to the research and practitioner communities, a broad readership that includes students, application engineers, and research scientists will find material that is of interest.

Model Predictive Control in the Process Industry

Eduardo F. Camacho,Carlos A. Bordons
Model Predictive Control in the Process Industry Book PDF

Author : Eduardo F. Camacho,Carlos A. Bordons
Publisher : Springer Science & Business Media
Page : 250 pages
File Size : 52,8 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781447130086

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Model Predictive Control in the Process Industry by Eduardo F. Camacho,Carlos A. Bordons Pdf

Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

Innovations in Intelligent Machines-5

Valentina Emilia Balas,Petia Koprinkova-Hristova,Lakhmi C. Jain
Innovations in Intelligent Machines 5 Book PDF

Author : Valentina Emilia Balas,Petia Koprinkova-Hristova,Lakhmi C. Jain
Publisher : Springer
Page : 261 pages
File Size : 41,6 Mb
Release : 2014-05-22
Category : Technology & Engineering
ISBN : 9783662433706

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Innovations in Intelligent Machines-5 by Valentina Emilia Balas,Petia Koprinkova-Hristova,Lakhmi C. Jain Pdf

This research monograph presents selected areas of applications in the field of control systems engineering using computational intelligence methodologies. A number of applications and case studies are introduced. These methodologies are increasing used in many applications of our daily lives. Approaches include, fuzzy-neural multi model for decentralized identification, model predictive control based on time dependent recurrent neural network development of cognitive systems, developments in the field of Intelligent Multiple Models based Adaptive Switching Control, designing military training simulators using modelling, simulation, and analysis for operational analyses and training, methods for modelling of systems based on the application of Gaussian processes, computational intelligence techniques for process control and image segmentation technique based on modified particle swarm optimized-fuzzy entropy.

Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty

Vassilis M. Charitopoulos
Uncertainty aware Integration of Control with Process Operations and Multi parametric Programming Under Global Uncertainty Book PDF

Author : Vassilis M. Charitopoulos
Publisher : Springer Nature
Page : 285 pages
File Size : 51,5 Mb
Release : 2020-02-05
Category : Science
ISBN : 9783030381370

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Uncertainty-aware Integration of Control with Process Operations and Multi-parametric Programming Under Global Uncertainty by Vassilis M. Charitopoulos Pdf

This book introduces models and methodologies that can be employed towards making the Industry 4.0 vision a reality within the process industries, and at the same time investigates the impact of uncertainties in such highly integrated settings. Advances in computing power along with the widespread availability of data have led process industries to consider a new paradigm for automated and more efficient operations. The book presents a theoretically proven optimal solution to multi-parametric linear and mixed-integer linear programs and efficient solutions to problems such as process scheduling and design under global uncertainty. It also proposes a systematic framework for the uncertainty-aware integration of planning, scheduling and control, based on the judicious coupling of reactive and proactive methods. Using these developments, the book demonstrates how the integration of different decision-making layers and their simultaneous optimisation can enhance industrial process operations and their economic resilience in the face of uncertainty.

Automotive Model Predictive Control

Luigi Del Re,Frank Allgöwer,Luigi Glielmo,Carlos Guardiola,Ilya Kolmanovsky
Automotive Model Predictive Control Book PDF

Author : Luigi Del Re,Frank Allgöwer,Luigi Glielmo,Carlos Guardiola,Ilya Kolmanovsky
Publisher : Springer
Page : 290 pages
File Size : 50,9 Mb
Release : 2010-03-11
Category : Technology & Engineering
ISBN : 9781849960717

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Automotive Model Predictive Control by Luigi Del Re,Frank Allgöwer,Luigi Glielmo,Carlos Guardiola,Ilya Kolmanovsky Pdf

Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility – at the price of complexity and di?cult tuning. The progressive evolution has been mainly ledby speci?capplicationsandshorttermtargets,withthe consequencethat automotive control is to a very large extent more heuristic than systematic. Product requirements are still increasing and new challenges are coming from potentially huge markets like India and China, and against this ba- ground there is wide consensus both in the industry and academia that the current state is not satisfactory. Model-based control could be an approach to improve performance while reducing development and tuning times and possibly costs. Model predictive control is a kind of model-based control design approach which has experienced a growing success since the middle of the 1980s for “slow” complex plants, in particular of the chemical and process industry. In the last decades, severaldevelopments haveallowedusing these methods also for “fast”systemsandthis hassupporteda growinginterestinitsusealsofor automotive applications, with several promising results reported. Still there is no consensus on whether model predictive control with its high requi- ments on model quality and on computational power is a sensible choice for automotive control.