Model-Based Optimizing Control and Estimation Using Modelica Model

Abstract

This paper reports on experiences from case studies in using Modelica/Dymola models interfaced to control and optimization software, as process models in real time process control applications. Possible applications of the integrated models are in state- and parameter estimation and nonlinear model predictive control. It was found that this approach is clearly possible, providing many advantages over modeling in low-level programming languages. However, some effort is required in making the Modelica models accessible to NMPC software.

Keywords

• Non-linear model predictive control
• state estimation
• Modelica
• offshore oil- and gas production