Publications
A General Framework for Sensitivity-Based Optimal Control and State Estimation
New modelling and optimization platforms have enabled the creation of frameworks for solution strategies that are based on solving sequences of dynamic optimization problems. This study demonstrates the application of the Python-based Pyomo platform as a basis for formulating and solving Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) problems, which enables fast on-line computations through large-scale nonlinear optimization and Nonlinear Programming (NLP) sensitivity. We describe these underlying approaches and sensitivity computations, and showcase the implementation of the framework with large DAE case studies including tray-by-tray distillation models and Bubbling Fluidized Bed Reactors (BFB).