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Multi-parametric Programming & Multi-parametric Model-based Control Books

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Process Systems Engineering (PSE) Book Series, Wiley-VCH
Edited by Efstratios N. Pistikopoulos, Michael C. Georgiadis and Vivek Dua

Process Systems Engineering offers an integrated and interdisciplinary approach towards the development of methodologies and tools for modeling, design, control and optimization of enterprise-wide, process, maufacturing, energy and other such complex systems. A key theme is the systematic management of complexity in systems involving uncertainty across different time and length scales. To address this formidable challenge, the multi-disciplinary expertise of mechanical, control, chemical, molecular and biological engineers, operations researchers, mathematical programming specialists and computer scientists is required.

Process Systems Engineering
Volume 1: Multi-Parametric Programming - Theory, Algorithms, and Applications
Volume Editors: Pistikopoulos, Georgiadis, Dua
ISBN: 978-3-527-31691-5
This Volume presents an in depth account of recent novel theoretical and algorithmic developments for different types of multiparametric programming problems, as well as describes a number of versatile engineering applications in areas such as design and optimization under uncertainty, energy and environmental analysis, multi-criteria optimization and model-based control (which is the subject of Volume 2 of this series). Multiparametric programming provides optimization based tools to systematically analyze the effect of uncertainty and variability in mathematical progrgamming problems, which involve a linear, nonlinear or mixed continuous and integer mathematical model, an objective function, a set of constraints, and in which a number of parameters in the model vary between lower and upper bounds. The aim is to obtain explicit analytical, exact or approximate, expressions of the objective function and the optimization variables as a function of these parameters, and the regions in the space of the parameters where these optimal expressions are valid.

Part I - Theory and algorithms

Part II - Applications

Process Systems Engineering
Volume 2: Multi-Parametric Model-Based Control - Theory and Applications
Volume Editors: Pistikopoulos, Georgiadis, Dua
ISBN: 978-3-527-31692-2
This Volume presents recent exciting developments for the solution of model-based predictive control problems by multiparametric programming algorithms and tools, described in Volume 1 of this series. Model-based predictive control (MPC) is the advanced control technology of choice in process systems engineering applications, for on-line control and real-time optimization. It relies on the computation of the optimal control input actions by repeatedly solving on-line and open or closed loop optimal control problems at the time instant when a state measurement or estimation becomes available. However, the capabilities of this technology are restricted mainly by the often extensive and computationally demanding on-line calculations, which make MPC mostly suitable for large-scale, expensive and slowly varying systems. This book describes in detail how these shortcomings can be effectively overcome by employing the novel multiparametric programming theory and algorithms of Volume 1, for a wide range of MPC problems, including hybrid and robust control.


Part I - Theory

Part II - Applications

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Professor Stratos Pistikopoulos, PhD CEng FIChemE
Director
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
Roderic Hill Building
South Kensington Campus, London SW7 2AZ,UK
tel - +44-20-75946620 (direct)
      +44-20-75946627 (PA, Ms. Monica Nestor,
                                   [log in to unmask])
mob - +44-7803-205551
fax - + 44-20-75941129 / +44-20-75946606
email: [log in to unmask]
http://www.imperial.ac.uk/people/e.pistikopoulos
http://www.cpse.ic.ac.uk/about/staff_detail1.php?recordID=enp
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Process Systems Engineering
Volume 1: Multi-Parametric Programming
Process Systems Engineering
Volume 2: Multi-Parametric Model-Based Control
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