6 edition of **Model Predictive Control (Advanced Textbooks in Control and Signal Processing)** found in the catalog.

- 228 Want to read
- 18 Currently reading

Published
**April 2007**
by Springer
.

Written in English

- Automatic control engineering,
- Technology,
- Mathematics,
- Science/Mathematics,
- Applied,
- Constraint,
- Control Engineering,
- Industrial Application,
- Model Predictive Control,
- Modelling,
- Robustness,
- Technology / Engineering / Electrical,
- Engineering - General,
- Predictive control

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 405 |

ID Numbers | |

Open Library | OL8974347M |

ISBN 10 | 1852336943 |

ISBN 10 | 9781852336943 |

of model predictive control (MPC) has seen tremendous progress. First and foremost, the algorithms and high-level software available for solv-ing challenging nonlinear optimal control problems have advanced sig-niﬁcantly. For this reason, we have added a new chapter, Chapter 8, “Numerical Optimal Control,” and coauthor, Professor Moritz M. Nowadays, optimality is a major concern in modern controlled systems, and since optimality generally steers the systems to the boundary of their admissible operational domain, no control designer can afford ignoring Model Predictive Control (MPC).

out of 5 stars A must have if you are interested in theoretical Model Predictive Control Reviewed in the United States on Septem This book has a formal treatment of MPC, with proofs of stability using rigorous Lyapunov theory/5(5). 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 Cited by:

In this original book on model predictive control (MPC) for power electronics, the focus is put on high-power applications with multilevel converters operating at switching frequencies well below 1 kHz, such as medium-voltage drives and modular multi-level converters. Consisting of two main parts, the first offers a detailed review of three. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and : Springer International Publishing.

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The book is of interest as an introduction to model predictive control, and a merit is the special presentation, connecting the subject intimately with industrial situations." (A.

Akutowicz, Zentralblatt MATH, Vol. )Cited by: 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.

Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems.

The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance.

Moving on to robust predictive Cited by: Model Predictive Control. Edited by: Tao Zheng. ISBNPDF ISBNPublished Cited by: Here are some examples of good books in Model Predictive control: Predictive control with constraints.

by owski. - Model Predictive Control, by. In recent years Model Predictive Control (MPC) schemes have established themselves as the preferred control strategy for a large number of processes. Their ability to handle constraints and multivariable processes and their intuitive way of posing the pro cess control problem in the time domain are two reasons for their popularity.

This volume by authors of international repute provides an. 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. Model predictive control (MPC) is an advanced method of process control that is used to control a process Model Predictive Control book satisfying a set of constraints.

It has been in use in the process industries in chemical plants Model Predictive Control book oil refineries since the s. In recent years it has also been used in power system balancing models and in power predictive controllers rely on dynamic models of.

The book is of interest as an introduction to model predictive control, and a merit is the special presentation, connecting the subject intimately with industrial situations." (A.

Akutowicz, Zentralblatt MATH, Vol. ). The method applies a Gaussian process (GP) model to learn the optimal control policy generated by a recently developed fast model predictive control (MPC) algorithm based on an LPV embedding of.

In this chapter a model predictive controller (MPC) will be used to control the active optical filter to achieve optimal output power based on PV module temperature.

A PV module thermoelectrical model is introduced in Section and is used as a testbed to demonstrate effectiveness. 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. Model-based predict ive control: a practical approach / by J.A.

Rossite r. Ñ (Control series) Includes bibliographical references and index. ISBN (alk. paper) 1. Predictive control. Control theory. Title. CRC Press control series TJR67 Ñdc21 disclaimer Page 1 Monday, Overview of Model Predictive Control.

A block diagram of a model predictive control sys-tem is shown in Fig. A process model is used to predict the current values of the output variables. The residuals, the differences between the actual and pre-dicted outputs, serve as the feedback signal to a. Predic-tion. block. Model Predictive Control of Wind Energy Conversion Systems addresses the predicative control strategy that has emerged as a promising digital control tool within the field of power electronics, variable-speed motor drives, and energy conversion systems.

The authors provide a comprehensive analysis on the model predictive control of power converters employed in a wide. The application Model Predictive Control (MPC) controls electrical energy with the use of power converters and offers a highly flexible alternative to the use of modulators and linear controllers.

This new approach takes into account the discrete and nonlinear nature of the power converters and. From power plants to sugar refining, model predictive control (MPC) schemes have established themselves as the preferred control strategies for a wide variety of processes.

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. Model Predictive Control (Advanced Textbooks in Control and Signal Processing) - Kindle edition by Camacho, Eduardo F., Bordons Alba, Carlos. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Model Predictive Control (Advanced Textbooks in Control and Signal Processing).4/5(6).

The book is aimed at a wide readership ranging from industrial control engineers to graduate students in the process and control disciplines.” (IEEE Control Systems Magazine, Vol.

30, August, ) “The book gives an introduction to Model Predictive Control (MPC), and recent developments in design and implementation. /5(3). 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 Cited by: 2. Linear Quadratic Optimal Control and Model Pridictive Control (a) APC_Part_7_LQG_and_MPC (a) APC_Part_7_LQG_and_MPC: kb: Linear Quadratic Optimal Control and Model Pridictive Control (b) LQG_MPC_Notes (b) LQG_MPC_Notes: kb.

This project thesis provides a brief overview of Model Predictive Control (MPC).A brief history of industrial model predictive control technology has been presented first followed by a some concepts like the receding horizon, moves etc.

which form the basis of the MPC. It follows the Optimization problem which ultimately leads to the description of the Dynamic Matrix Control (DMC).The book proposes a simple predictive controller where the control laws are given in clear text that requires no calculations.

Adjustment, reduced to one or two parameters, is particularly easy, by means of charts, thus allowing the operator to choose the horizon according to the desired performances.