Advanced Model Predictive Control

Edited by: Tao Zheng



ISBN 978-953-307-298-2, Hard cover, 418 pages

Publisher: InTech

Publication date: July 2011

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.

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Frontiers of Model Predictive Control

Frontiers of Model Predictive Control







Edited by: Tao Zheng

ISBN 978-953-51-0119-2,

Hard cover, 156 pages

Publisher: InTech

Publication date: February 2012

Subject: Control Engineering

Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance, but it is can also be seen as a term denoting a natural control strategy that matches the human thought form most closely. Half a century after its birth, it has been widely accepted in many engineering fields and has brought much benefit to us. The purpose of the book is to show the recent advancements of MPC to the readers, both in theory and in engineering. The idea was to offer guidance to researchers and engineers who are interested in the frontiers of MPC. The examples provided in the first part of this exciting collection will help you comprehend some typical boundaries in theoretical research of MPC. In the second part of the book, some excellent applications of MPC in modern engineering field are presented. With the rapid development of modeling and computational technology, we believe that MPC will remain as energetic in the future.

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Predictive control with constraintes

Predictive Control, or Model-Based Predictive Control ('MPC' or 'MBPC') as it is

sometimes known, is the only advanced control technique- that is, more advanced than standard PID control - to have had a significant and widespread impact on industrial process control. The main reason for this is that it is

@ The only generic control technology which can deal routinely with equipment and

safety constraints.

Operation at or near such constraints is necessary for the most profitable or most efficient operation in many cases. The penetration of predictive control into industrial practice has also been helped by the facts that .. Its underlying idea is easy to understand, Its basic formulation extends to multivariable plants with almost no modification, sq It is more powerful than PID control, even for single loops without constraints,without being much more difficult to tune, even on 'difficult' loops such as those containing long time delays.

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Nonlinear Predictive Control theory and practice

Model based predictive control has proved to be a fertile area of research but above all has gained enormous success with industry, especially in the context of process control. Non-linear model based predictive control is of particular interest as this best represents the dynamics of most real plant, and this book collects together the important results which have emerged in this field which are illustrated by means of simulations on industrial models. In particular there are contributions on feedback linearisation, differential flatness, control Lyapunov functions, output feedback, and neural networks. The international contributors to the book are all respected leaders within the field, which makes for essential reading for advanced students, researchers and industrialists in the field of control of complex systems.

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Nonlinear model predictive control Towards New Challenging Applications

Over the past few years significant progress has be achieved in the field of nonlinear model predictive control (NMPC), also refered 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 contribution of 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 that will be presented at the International Workshop on Assessment an Future Directions of Nonlinear Model Predictive Control that will took place from September 5 to 9, 2008, in Pavia, Italy.

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Model Predictive Control

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Model predictive control (tao)

 

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.

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