Multi-Parametric Model-Based Control - Theory and Applications (Hardcover, Volume 2)

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.

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