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This book focuses on distributed and economic Model Predictive
Control (MPC) with applications in different fields. MPC is one of
the most successful advanced control methodologies due to the
simplicity of the basic idea (measure the current state, predict
and optimize the future behavior of the plant to determine an input
signal, and repeat this procedure ad infinitum) and its capability
to deal with constrained nonlinear multi-input multi-output
systems. While the basic idea is simple, the rigorous analysis of
the MPC closed loop can be quite involved. Here, distributed means
that either the computation is distributed to meet real-time
requirements for (very) large-scale systems or that distributed
agents act autonomously while being coupled via the constraints
and/or the control objective. In the latter case, communication is
necessary to maintain feasibility or to recover system-wide optimal
performance. The term economic refers to general control tasks and,
thus, goes beyond the typically predominant control objective of
set-point stabilization. Here, recently developed concepts like
(strict) dissipativity of optimal control problems or turnpike
properties play a crucial role. The book collects research and
survey articles on recent ideas and it provides perspectives on
current trends in nonlinear model predictive control. Indeed, the
book is the outcome of a series of six workshops funded by the
German Research Foundation (DFG) involving early-stage career
scientists from different countries and from leading European
industry stakeholders.
This book focuses on distributed and economic Model Predictive
Control (MPC) with applications in different fields. MPC is one of
the most successful advanced control methodologies due to the
simplicity of the basic idea (measure the current state, predict
and optimize the future behavior of the plant to determine an input
signal, and repeat this procedure ad infinitum) and its capability
to deal with constrained nonlinear multi-input multi-output
systems. While the basic idea is simple, the rigorous analysis of
the MPC closed loop can be quite involved. Here, distributed means
that either the computation is distributed to meet real-time
requirements for (very) large-scale systems or that distributed
agents act autonomously while being coupled via the constraints
and/or the control objective. In the latter case, communication is
necessary to maintain feasibility or to recover system-wide optimal
performance. The term economic refers to general control tasks and,
thus, goes beyond the typically predominant control objective of
set-point stabilization. Here, recently developed concepts like
(strict) dissipativity of optimal control problems or turnpike
properties play a crucial role. The book collects research and
survey articles on recent ideas and it provides perspectives on
current trends in nonlinear model predictive control. Indeed, the
book is the outcome of a series of six workshops funded by the
German Research Foundation (DFG) involving early-stage career
scientists from different countries and from leading European
industry stakeholders.
Model Predictive Control (MPC) can be dated back to the 1960s, and
can now be regarded as a mature control method, which has had
significant impact on industrial process control. It is applied in
many control systems and has been extended to include non-linear
dynamics and non-convex constraints. Of increasing importance in
all such control systems in the economic benefits within the design
of the system. Traditionally, the so-called control pyramid has
been the main technique to do this, whereby economic targets are
translated into setpoints and reference trajectories, which are in
turn stabilized by control techniques such as MPC. At the same
time, in process systems engineering and other fields of
application, one aims at economic process operation and much
attention has been given to this and the term Economic Model
Predictive Control (EMPC) has been coined. Economic Nonlinear Model
Predictive Control provides a concise overview of different
approaches on the question of stability and optimality in different
formulations of EMPC. It is the first monograph to cover approaches
both with and without terminal constraints and end penalties, and
turnpike/dissipativity-based settings as well as Lyapunov-based
approaches. This monograph is an accessible tutorial on the
state-of-the-art in model predictive control. Students and
researchers will find a clear exposition of current knowledge upon
which they can build their own research.
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