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Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred-
tive control with respect to both theoretical and application
aspects. Over these 30 years, model predictive control for linear
systems has been widely applied, especially in the area of process
control. However, today's applications often require driving the
process over a wide region and close to the boundaries of -
erability, while satisfying constraints and achieving near-optimal
performance. Consequently, the application of linear control
methods does not always lead to satisfactory performance, and here
nonlinear methods must be employed. This is one of the reasons why
nonlinear model predictive control (NMPC) has - joyed signi?cant
attention over the past years,with a number of recent advances on
both the theoretical and application frontier. Additionally, the
widespread availability and steadily increasing power of today's
computers, as well as the development of specially tailored
numerical solution methods for NMPC, bring
thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This
has led to a series of new, exciting developments, along with new
challenges in the area of NMPC.
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