Economic Model Predictive Control

Economic Model Predictive Control
Author :
Publisher : Springer
Total Pages : 311
Release :
ISBN-10 : 9783319411088
ISBN-13 : 331941108X
Rating : 4/5 (08X Downloads)

Book Synopsis Economic Model Predictive Control by : Matthew Ellis

Download or read book Economic Model Predictive Control written by Matthew Ellis and published by Springer. This book was released on 2016-07-27 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes.In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.


Economic Model Predictive Control Related Books

Economic Model Predictive Control
Language: en
Pages: 311
Authors: Matthew Ellis
Categories: Technology & Engineering
Type: BOOK - Published: 2016-07-27 - Publisher: Springer

DOWNLOAD EBOOK

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key th
Economic Nonlinear Model Predictive Control
Language: en
Pages: 118
Authors: Timm Faulwasser
Categories: Predictive control
Type: BOOK - Published: 2018-01-12 - Publisher: Foundations and Trends in Systems and Control

DOWNLOAD EBOOK

In recent years, Economic Model Predictive Control (EMPC) has received considerable attention of many research groups. The present tutorial survey summarizes st
Economic Model Predictive Control
Language: en
Pages: 68
Authors: Helen Durand
Categories:
Type: BOOK - Published: 2018-06-19 - Publisher: Foundations and Trends (R) in Systems and Control

DOWNLOAD EBOOK

Economic Model Predictive Control (EMPC) is a control strategy that moves process operation away from the steady-state paradigm toward a potentially time-varyin
Model Predictive Control in the Process Industry
Language: en
Pages: 250
Authors: Eduardo F. Camacho
Categories: Technology & Engineering
Type: BOOK - Published: 2012-12-06 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

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 th
Handbook of Model Predictive Control
Language: en
Pages: 693
Authors: Saša V. Raković
Categories: Science
Type: BOOK - Published: 2018-09-01 - Publisher: Springer

DOWNLOAD EBOOK

Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the contr