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.
General
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