Automotive Model Predictive Control

Author : Luigi Del Re,Frank Allgöwer,Luigi Glielmo,Carlos Guardiola,Ilya Kolmanovsky
Publisher : Springer Science & Business Media
Page : 291 pages
File Size : 48,8 Mb
Release : 2010-03-11
Category : Technology & Engineering
ISBN : 9781849960700

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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.

Author : Luigi Del Re,Frank Allg Wer,Luigi Glielmo
Publisher : Unknown
Page : 306 pages
File Size : 40,8 Mb
Release : 2010-09-10
Category : Electronic
ISBN : 1849960720

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Automotive Model Predictive Control by Luigi Del Re,Frank Allg Wer,Luigi Glielmo Pdf

Author : Saša V. Raković,William S. Levine
Publisher : Springer
Page : 692 pages
File Size : 47,5 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.

Author : Craig Earl Beal
Publisher : Stanford University
Page : 161 pages
File Size : 45,5 Mb
Release : 2011
Category : Electronic
ISBN : STANFORD:rv794bn8004

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Applications of Model Predictive Control to Vehicle Dynamics for Active Safety and Stability by Craig Earl Beal Pdf

Each year in the United States, thousands of lives are lost as a result of loss of control crashes. Production driver assistance systems such as electronic stability control (ESC) have been shown to be highly effective in preventing many of these automotive crashes, yet these systems rely on a sensor suite that yields limited information about the road conditions and vehicle motion. Furthermore, ESC systems rely on gains and thresholds that are tuned to yield good performance without feeling overly restrictive to the driver. This dissertation presents an alternative approach to providing stabilization assistance to the driver which leverages additional information about the vehicle and road that may be obtained with advanced estimation techniques. This new approach is based on well-known and robust vehicle models and utilizes phase plane analysis techniques to describe the limits of stable vehicle handling, alleviating the need for hand tuning of gains and thresholds. The resulting state space within the computed handling boundaries is referred to as a safe handling envelope. In addition to the boundaries being straightforward to calculate, this approach has the benefit of offering a way for the designer of the system to directly adjust the controller to accomodate the preferences of different drivers. A model predictive control structure capable of keeping the vehicle within the safe handling boundaries is the final component of the envelope control system. This dissertation presents the design of a controller that is capable of smoothly and progressively augmenting the driver steering input to enforce the boundaries of the envelope. The model predictive control formulation provides a method for making trade-offs between enforcing the boundaries of the envelope, minimizing disruptive interventions, and tracking the driver's intended trajectory. Experiments with a steer-by-wire test vehicle demonstrate that the model predictive envelope control system is capable of operating in conjunction with a human driver to prevent loss of control of the vehicle while yielding a predictable vehicle trajectory. These experiments considered both the ideal case of state information from a GPS/INS system and an a priori friction estimate as well as a real-world implementation estimating the vehicle states and friction coefficient from steering effort and inertial sensors. Results from the experiments demonstrated a controller that is tolerant of vehicle and tire parameterization errors and works well over a wide range of conditions. When real time sensing of the states and friction properties is enabled, the results show that coupling of the controller and estimator is possible and the model predictive control structure provides a mechanism for minimizing undesirable coupled dynamics through tuning of intuitive controller parameters. The model predictive control structure presented in this dissertation may also be considered as a general framework for vehicle control in conjunction with a human driver. The structure utilized for envelope control may also be used to restrict other vehicle states for safety and stability. Results are presented in this dissertation to show that a model predictive controller can coordinate a secondary actuator to alter the planar states and reduce the energy transferred into the roll modes of the vehicle. The systematic approach to vehicle stabilization presented in this dissertation has the potential to improve the design methodology for future systems and form the basis for the inclusion of more advanced functions as sensing and computing capabilities improve. The envelope control system presented here offers the opportunity to advance the state of the art in stabilization assistance and provides a way to help drivers of all skill levels maintain control of their vehicle.

Author : Corrine Wade
Publisher : Unknown
Page : 168 pages
File Size : 41,6 Mb
Release : 2015
Category : Predictive control
ISBN : 1634638875

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Model Predictive Control by Corrine Wade Pdf

Although industrial processes are inherently nonlinear, many contributions for controller design for those plants are based on the assumption of a linear model of the system. However, in some cases it is difficult to represent a given process using a linear model. Model Predictive Control (MPC) is an optimal control approach which can effectively deal with constraints and multivariable processes in industries. Because of its advantages, MPC has been widely applied in automotive and process control communities. This book discusses the theory, practices and future challenges of model predictive control.

Author : Tao Zheng
Publisher : BoD – Books on Demand
Page : 434 pages
File Size : 54,5 Mb
Release : 2011-07-05
Category : Technology & Engineering
ISBN : 9789533072982

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Advanced Model Predictive Control by Tao Zheng Pdf

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.

Author : Eduardo F. Camacho,Carlos A. Bordons
Publisher : Springer Science & Business Media
Page : 250 pages
File Size : 51,5 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.

Author : Francesco Borrelli,Alberto Bemporad,Manfred Morari
Publisher : Cambridge University Press
Page : 447 pages
File Size : 50,5 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).

Author : Thivaharan Albin Rajasingham
Publisher : Springer Nature
Page : 330 pages
File Size : 43,6 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.

Author : Craig Earl Beal
Publisher : Unknown
Page : 128 pages
File Size : 52,5 Mb
Release : 2011
Category : Electronic
ISBN : OCLC:747192364

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Applications of Model Predictive Control to Vehicle Dynamics for Active Safety and Stability by Craig Earl Beal Pdf

Each year in the United States, thousands of lives are lost as a result of loss of control crashes. Production driver assistance systems such as electronic stability control (ESC) have been shown to be highly effective in preventing many of these automotive crashes, yet these systems rely on a sensor suite that yields limited information about the road conditions and vehicle motion. Furthermore, ESC systems rely on gains and thresholds that are tuned to yield good performance without feeling overly restrictive to the driver. This dissertation presents an alternative approach to providing stabilization assistance to the driver which leverages additional information about the vehicle and road that may be obtained with advanced estimation techniques. This new approach is based on well-known and robust vehicle models and utilizes phase plane analysis techniques to describe the limits of stable vehicle handling, alleviating the need for hand tuning of gains and thresholds. The resulting state space within the computed handling boundaries is referred to as a safe handling envelope. In addition to the boundaries being straightforward to calculate, this approach has the benefit of offering a way for the designer of the system to directly adjust the controller to accomodate the preferences of different drivers. A model predictive control structure capable of keeping the vehicle within the safe handling boundaries is the final component of the envelope control system. This dissertation presents the design of a controller that is capable of smoothly and progressively augmenting the driver steering input to enforce the boundaries of the envelope. The model predictive control formulation provides a method for making trade-offs between enforcing the boundaries of the envelope, minimizing disruptive interventions, and tracking the driver's intended trajectory. Experiments with a steer-by-wire test vehicle demonstrate that the model predictive envelope control system is capable of operating in conjunction with a human driver to prevent loss of control of the vehicle while yielding a predictable vehicle trajectory. These experiments considered both the ideal case of state information from a GPS/INS system and an a priori friction estimate as well as a real-world implementation estimating the vehicle states and friction coefficient from steering effort and inertial sensors. Results from the experiments demonstrated a controller that is tolerant of vehicle and tire parameterization errors and works well over a wide range of conditions. When real time sensing of the states and friction properties is enabled, the results show that coupling of the controller and estimator is possible and the model predictive control structure provides a mechanism for minimizing undesirable coupled dynamics through tuning of intuitive controller parameters. The model predictive control structure presented in this dissertation may also be considered as a general framework for vehicle control in conjunction with a human driver. The structure utilized for envelope control may also be used to restrict other vehicle states for safety and stability. Results are presented in this dissertation to show that a model predictive controller can coordinate a secondary actuator to alter the planar states and reduce the energy transferred into the roll modes of the vehicle. The systematic approach to vehicle stabilization presented in this dissertation has the potential to improve the design methodology for future systems and form the basis for the inclusion of more advanced functions as sensing and computing capabilities improve. The envelope control system presented here offers the opportunity to advance the state of the art in stabilization assistance and provides a way to help drivers of all skill levels maintain control of their vehicle.

Author : Lalo Magni,Davide Martino Raimondo,Frank Allgöwer
Publisher : Springer
Page : 576 pages
File Size : 47,8 Mb
Release : 2009-05-18
Category : Technology & Engineering
ISBN : 9783642010941

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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.

Author : Lars Grüne,Jürgen Pannek
Publisher : Springer
Page : 456 pages
File Size : 46,5 Mb
Release : 2016-11-09
Category : Technology & Engineering
ISBN : 9783319460246

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Nonlinear Model Predictive Control by Lars Grüne,Jürgen Pannek Pdf

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 : Liuping Wang
Publisher : Springer Science & Business Media
Page : 378 pages
File Size : 54,6 Mb
Release : 2009-02-14
Category : Technology & Engineering
ISBN : 9781848823310

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Model Predictive Control System Design and Implementation Using MATLAB® by Liuping Wang Pdf

Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.

Author : Herwig Koppauer
Publisher : Unknown
Page : 128 pages
File Size : 53,8 Mb
Release : 2019
Category : Electronic
ISBN : 3844067515

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Nonlinear Model Predictive Control of an Automotive Waste Heat Recovery System by Herwig Koppauer Pdf

Author : Rainer Dittmar
Publisher : BoD – Books on Demand
Page : 214 pages
File Size : 51,5 Mb
Release : 2019-12-04
Category : Computers
ISBN : 9781838800956

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Model Predictive Control mit MATLAB und Simulink by Rainer Dittmar Pdf

Modellbasierte prädiktive Regelungen dienen der Lösung anspruchsvoller Aufgaben der Mehrgrößenregelung mit Beschränkungen der Stell- und Regelgrößen. Sie werden in der Industrie in vielen Bereichen erfolgreich eingesetzt. Mit der MPC ToolboxTM des Programmsystems MATLAB®/Simulink® steht ein Werkzeug zur Verfügung, das sowohl in der industriellen Praxis als auch an Universitäten und Hochschulen verwendet wird. Das vorliegende Buch gibt eine Übersicht über die Grundideen und Anwendungsvorteile des MPC-Konzepts. Es zeigt, wie mit Hilfe der Toolbox MPC-Regelungen entworfen, eingestellt und simuliert werden können. Ausgewählte Beispiele aus dem Bereich der Verfahrenstechnik demonstrieren mögliche Vorgehensweisen und vertiefen das Verständnis. Das Buch richtet sich an in der Industrie tätige Ingenieure, die MPC-Regelungen planen, entwickeln und betreiben, aber auch an Studierende technischer Fachdisziplinen, die in das Arbeitsgebiet MPC einsteigen wollen. Model Predictive Control (MPC) is used to solve challenging multivariable-constrained control problems. MPC systems are successfully applied in many different branches of industry. The MPC ToolboxTM of MATLAB®/Simulink® provides powerful tools for industrial MPC application, but also for education and research at technical universities. This book gives an overview of the basic ideas and advantages of the MPC concept. It shows how MPC systems can be designed, tuned, and simulated using the MPC Toolbox. Selected process engineering benchmark examples are used to demonstrate typical design approaches and help deepen the understanding of MPC technologies. The book is aimed at engineers in industry interested in the development and application of MPC systems, as well as students of different technical disciplines seeking an introduction into this field.This book gives an overview of the basic ideas and advantages of the MPC concept. It shows how MPC systems can be designed, tuned, and simulated using the MPC Toolbox. Selected process engineering benchmark examples are used to demonstrate typical design approaches and help deepen the understanding of MPC technologies. The book is aimed at engineers in industry interested in the development and application of MPC systems, as well as students of different technical disciplines seeking an introduction into this field.