Model-Based Optimizing Control and Estimation using Modelica Models
Lars Imsland,Pål Kittilsen,Tor Steinar Schei +2 more
- 07 Jan 2010
Vol. 31, Iss: 3, pp 107-121
TL;DR: 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, providing many advantages over modeling in low-level programming languages.
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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 eort is required in making the Modelica models accessible to NMPC software. Particular consideration is given to implementation of gradient computation for real-time dynamic optimization, where the dynamic models can be Modelica models. Analytical methods for gradient computation based on sensitivity integration are compared to nite dierence-based
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