Economic Model Predictive Control

Theory, Formulations and Chemical Process Applications

Nonfiction, Science & Nature, Technology, Automation, Science, Chemistry, Technical & Industrial
Cover of the book Economic Model Predictive Control by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides ISBN: 9783319411088
Publisher: Springer International Publishing Publication: July 27, 2016
Imprint: Springer Language: English
Author: Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
ISBN: 9783319411088
Publisher: Springer International Publishing
Publication: July 27, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

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.

More books from Springer International Publishing

Cover of the book Joining Technologies for Composites and Dissimilar Materials, Volume 10 by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Sensors and Instrumentation, Volume 5 by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Politics in Socrates' Alcibiades by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Introduction to Earnings Management by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Biomarkers for Endometriosis by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Non-Representational Geographies of Therapeutic Art Making by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Multi-Agent Based Simulation XVII by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Law and Economics in Europe and the U.S. by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Data Privacy Games by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Asymmetric Cell Division in Development, Differentiation and Cancer by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Actuarial Sciences and Quantitative Finance by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Making Sense of Quantum Mechanics by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Herpes Zoster: Postherpetic Neuralgia and Other Complications by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Wideband CMOS Receivers by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
Cover of the book Spinal Instability by Matthew Ellis, Jinfeng Liu, Panagiotis D. Christofides
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy