About the Book
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 ofcomplexity 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 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 systemsnew theoretical developments on hybrid model based controlmethods for obtaining the explicit robust model-based tracking controltheoretical frameworks for parametric dynamic optimization andrecent 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 systemsthe development of advanced model based control techniques for regulating the blood glucose for patients with Type 1 diabetesthe 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.