In this work, a computationally
efficient nonlinear model-based
control (NMBC) strategy is developed for a trajectory-tracking problem
in an acrylamide polymerization batch reactor. The performance of
NMBC is compared with that of nonlinear model predictive control (NMPC).
To estimate the reaction states, a nonlinear state estimator, an unscented
Kalman filter (UKF), is employed. Both algorithms are implemented
experimentally to track a time-varying temperature profile for an
acrylamide polymerization reaction in a lab-scale polymerization reactor.
It is shown that in the presence of state estimators the NMBC performs
significantly better than the NMPC algorithm in real time for the
batch reactor control problem.
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