integrative-toolkit-to-analyze-cellular-signals-forces-motion

Integrative Toolkit to Analyze Cellular Signals: Forces, Motion, Morphology, and Fluorescence

Quantitative assessment of cellular forces and motion advanced considerably over the last four decades. These advancements provided the framework to ...

West Virginia University

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.

Srinivas Palanki

Biography

JoVE Journal Publications

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