Fast nonlinear model predictive control: Formulation and industrial process applications

Computers & Chemical Engineering, Volume 51, 5 April 2013, Pages 55–64
CPC VIII
Fast nonlinear model predictive control: Formulation and industrial process applications
Rodrigo Lopez-Negrete (a), Fernando J. D’Amato (a), Lorenz T. Biegler (b), Aditya Kumar (a)
a GE Global Research, Niskayuna, NY 12309, USA
b Carnegie Mellon University, Pittsburgh, PA 15213, USA
Abstract
Nonlinear MPC provides a natural extension of model predictive control (MPC) to include nonlinear models for trajectory tracking and dynamic optimization. NMPC can include first principle models developed for off-line dynamic studies as well as nonlinear data-driven models. However, it requires the application of efficient large-scale optimization strategies to avoid computational delays and to ensure stability, robustness and superior performance.
Authors describe the application of the recently developed advanced step NMPC (asNMPC) strategy. The approach solves the detailed optimization problem in the background and applies a sensitivity-based update on-line. Two large-scale process case studies are presented: detailed distillation control and multi-stage operation for steam generation in a power plant.
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