This volume covers theoretical advances and developments, computational challenges and tools as well as applications in the area of multi-parametric model based control.
Part I is concerned with the presentation of algorithms for parametric model based control focusing on:
- novel frameworks for the derivation of explicit optimal control policies for continuous time-linear dynamic systems
- new theoretical developments on hybrid model based control
- methods for obtaining the explicit robust model-based tracking control
- theoretical frameworks for parametric dynamic optimization and
- recent developments for continuous-time systems
Part II presents a series of application in the following areas:
- the incorporation of advanced model based controllers in a simultaneous process design and control framework for complex separation systems
- the development of advanced model based control techniques for regulating the blood glucose for patients with Type 1 diabetes
- the design of model predictive and parametric controllers for anesthesia.
- the development of optimal control policies in a pilot plant exothermic reactor
The volume is intended for academics and researchers that carry out model based control research, industrial practitioners involved in the control of new and existing processes and products, policy makers, as well as for educational purposes both in academia and industry.
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Efstratios N. Pistikopoulos is a Professor of Chemical Engineering at Imperial College London and Director of its Centre for Process Systems Engineering (PSE). He graduated in Chemical Engineering from Aristotle University of Thessaloniki, Greece and gained a PhD from Carnegie Mellon University, USA. He has authored/ co-authored over 200 publications, holds editorial positions on several editorial boards and has been involved in over 50 major research projects and contracts. Prof. Pistikopoulos is co-founder and Director of two successful spin-off companies stemming from his research at Imperial, Process Systems Enterprise (PSE) Limited and Parametric Optimization Solutions (PAROS) Limited and consults widely to numerous process industry companies.
Michael C. Georgiadis is Head of the Process System Engineering Laboratory at the PSE, Imperial College London and is the manager for academic business development of Process Systems Enterprise Ltd in Thessaloniki, Greece. He obtained his Chemical Engineering degree from Aristotle University of Thessaloniki, Greece and a MSc and PhD from Imperial College London. Dr. Georgiadis has authored/ co-authored over 55 papers and two books. He has a long experience in the management and participation of more than 20 collaborative research contracts and projects.
Vivek Dua is a Lecturer in the Department of Chemical Engineering at University College London. He holds a degree in Chemical Engineering from Panjab University, Chandigarh, India and MTech in chemical engineering from the Indian Institute of Technology, Kanpur. He joined Kinetics Technology India Ltd. as a Process Engineer before moving to Imperial College London, where he obtained his PhD in Chemical Engineering. He was an Assistant Professor in the Department of Chemical Engineering at Indian Institute of Technology, Delhi before joining University College London. He is a co-founder of Parametric Optimization Solutions (PAROS) Ltd.
Process Systems Enterprise (PSE), provider of the gPROMS advanced process simulation and modelling environment, is the 2007 winner of the Royal Academy of Engineering's MacRobert Award. The award, the UK's most prestigious for engineering, recognises the successful development of innovative ideas. The PSE team was presented with the MacRobert gold medal by HRH Prince Philip.
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 applications, for on-line control and real-time optimization. It relies in the computation of the optimal control input actions by repeteadly solving on-line and open or closed loop optimal control problem 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 effcetively overcome by employing the novel multi-parametric programming theory and algorithms of volume 1, for a wide range of MPC problems, including hybrid and robust control.
The book is intended for academics, researchers and control practitioners who are involved on contemporary control studies, the design and implementation of control strategies, and the development of control hardware for embedded systems applications, as well as for educational purposes both in academia and industry.
The Process Systems Engineering (PSE) Series offers an integrated and interdisciplinary approach towards the development of methodologies and tools for modeling, design, control and optimization of enterprise-wide, process, manufacturing, 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.
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 applications, for on-line control and real-time optimization. It relies in the computation of the optimal control input actions by repeteadly solving on-line and open or closed loop optimal control problem 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 effcetively overcome by employing the novel multi-parametric programming theory and algorithms of volume 1, for a wide range of MPC problems, including hybrid and robust control.
The book is intended for academics, researchers and control practitioners who are involved on contemporary control studies, the design and implementation of control strategies, and the development of control hardware for embedded systems applications, as well as for educational purposes both in academia and industry.
The Process Systems Engineering (PSE) Series offers an integrated and interdisciplinary approach towards the development of methodologies and tools for modeling, design, control and optimization of enterprise-wide, process, manufacturing, 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.
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