Model Based Predictive Control
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Model Based Predictive Control
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Author : J.A. Rossiter
language : en
Publisher: CRC Press
Release Date : 2017-07-12
Model Based Predictive Control written by J.A. Rossiter and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-12 with Technology & Engineering categories.
Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.
Automotive Model Predictive Control
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Author : Luigi Del Re
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-03-11
Automotive Model Predictive Control written by Luigi Del Re and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-11 with Technology & Engineering categories.
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.
Model Predictive Control In The Process Industry
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Author : Eduardo F. Camacho
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Model Predictive Control In The Process Industry written by Eduardo F. Camacho and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
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.
Advances In Model Based Predictive Control
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Author : David Clarke
language : en
Publisher: Oxford Science Publications
Release Date : 1994
Advances In Model Based Predictive Control written by David Clarke and has been published by Oxford Science Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Science categories.
Model based predictive control (MBPC) is arguably the most important approach to the advance control of complex interacting industrial processes. Unique among modern theories, MBPC can handle real-time state and actuator constraints in a natural way, enabling plants to maximize their profits. In addition, the wide range of model structures, prediction horizons, and optimization criteria allows for tailor-made MBPC applications--whether they be for high-speed machine tools or large-scale industrial processes. This timely edited volume, based on a conference held at Oxford University and devoted exclusively to MBPC, brings the field up to date with the latest theoretical and practical advances. Topics include how MBPC is expanding to include variants in the basic themes (such as new prediction and optimization approaches, nonlinear models, and two-dimensional problems), general stabilization ideas for constrained plant, and unsolved problems in MBPC. This excellent volume is the introduction to the theory, current applications, and hot research areas in MBPC that students and professionals in control systems have been waiting for.
Model Predictive Control
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Author : Ridong Zhang
language : en
Publisher: Springer
Release Date : 2018-08-14
Model Predictive Control written by Ridong Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-14 with Technology & Engineering categories.
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.
Advances In Model Based Predictive Control
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Author :
language : en
Publisher:
Release Date : 1993
Advances In Model Based Predictive Control written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Actuators categories.
Model Based Predictive Control Of Electric Drives
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Author : Arne Linder
language : en
Publisher: Cuvillier Verlag
Release Date : 2010-07-09
Model Based Predictive Control Of Electric Drives written by Arne Linder and has been published by Cuvillier Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-09 with Technology & Engineering categories.
For more than 20 years, the so-called field-oriented control is standard for controlled electric drive systems. Until now, the strategies based on this method fulfill completely the requirements of drive technology. However, due to the system characteristics, an arbitrary improvement of the controller properties is not possible. Predictive or precalculating control methods which need no controller cascade are an alternative. Main focus of this work is to examine model-based predictive controllers for their applicability in drive technology. These methods with their high prediction horizon are well-known from classic control theory and in process engineering they are applied with great success. Several strategies are presented, explained and evaluated, whereas, at the same time, the interested reader gets advice for the implementation of these methods. Since model-based predictive control is, until now, not very common in drive technology, this work also includes detailed derivations of the control algorithms.
Methods Of Model Based Process Control
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Author : R. Berber
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Methods Of Model Based Process Control written by R. Berber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their exploration of the latest trends in the area.
Predictive Functional Control
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Author : Jacques Richalet
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-05-13
Predictive Functional Control written by Jacques Richalet and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-13 with Technology & Engineering categories.
first industrial application of MPC was in 1973. A key motivation was to provide better performance than could be obtained with the widely-used PID controller whilst making it easy to replace the PID controller unit or module with his new algorithm. It was the advent of digital control technology and the use of software control algorithms that made this replacement easier and more acceptable to process engineers. A decade of industrial practice with PFC was reported in the archival literature by Jacques Richalet et al. in 1978 in an important seminal Automatica paper. Around this time, Cutler and Ramaker published the dynamic matrix control algorithm that also used knowledge of future reference signals to determine a sequence of control signal adjustment. Thus, the theoretical and practical development of predictive control methods was underway and subsequent developments included those of generalized predictive control, and the whole armoury of MPC methods. Jacques Richalet’s approach to PFC was to seek an algorithm that was: • easy to understand; • easy to install; • easy to tune and optimise. He sought a new modular control algorithm that could be readily used by the control-technician engineer or the control-instrument engineer. It goes without saying that this objective also forms a good market strategy.
Developments In Model Based Optimization And Control
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Author : Sorin Olaru
language : en
Publisher: Springer
Release Date : 2015-12-23
Developments In Model Based Optimization And Control written by Sorin Olaru and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-23 with Technology & Engineering categories.
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and design; and · applications to bioprocesses, multivehicle systems or energy management. The various contributions cover a subject spectrum including inverse optimality and more modern decentralized and cooperative formulations of receding-horizon optimal control. Readers will find fourteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms—fault detection, for example—and three chapters putting forward applications where the model-based optimization brings a novel perspective. Developments in Model-Based Optimization and Control is a selection of contributions expanded and updated from the Optimisation-based Control and Estimation workshops held in November 2013 and November 2014. It forms a useful resource for academic researchers and graduate students interested in the state of the art in predictive control. Control engineers working in model-based optimization and control, particularly in its bioprocess applications will also find this collection instructive.