Nonlinear Model Predictive Control (NMPC) is a control strategy based on repeatedly solving an optimal control problem. In this paper we present a new MPC framework for the JModelica.org platform, developed specifically for use in NMPC schemes. The new framework utilizes the fact that the optimal control problem to be solved does not change between solutions, thus decreasing the computation time needed to solve it.
The new framework is compared to the old optimization framework in JModelica.org in regards to computation time and solution obtained through a benchmark on a combined cycle power plant. The results show that the new framework obtains the same solution as the old framework, but in less than half the time.
"HSL. A collection of Fortran codes for large scale scientific computation.", 2013. URL http://www.hsl.rl.ac.uk/.
Johan Åkesson. Optimica—an extension of modelica supporting dynamic optimization. In 6th International Modelica Conference 2008, 2008.
Johan Åkesson, Karl-Erik Årzén, Magnus Gäfvert, Tove Bergdahl, and Hubertus Tummescheit. Modeling and optimization with Optimica and JModelica.org—languages and tools for solving large-scale dynamic optimization problems. Computers and Chemical Engineering, 34(11):1737–1749, November 2010.
Frank Allgöwer, Rolf Findeisen, and Zoltan K Nagy. Nonlinear model predictive control: From theory to application. J. Chin. Inst. Chem. Engrs, 35(3):299–315, 2004.
Joel Andersson. A General-Purpose Software Framework for Dynamic Optimization. PhD thesis, Arenberg Doctoral School, KU Leuven, October 2013.
Magdalena Axelsson. Nonlinear Model Predictive Control in JModelica.org. Master’s thesis, Department of Automatic Control, Lund University, Sweden, August 2015.
Karl Berntorp and Fredrik Magnusson. Hierarchical predictive control for ground-vehicle manuevering. In 2015 American Control Conference, 2015.
Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004. ISBN 0521833787.
Francesco Casella, Filippo Donida, and Johan Åkesson. Object-oriented modeling and optimal control: A case study in power plant start-up. In 18th IFACWorld Congress, 2011.
Mats Vande Cavey, Roel De Coninck, and Lieve Helsen. Setting up a framework for model predictive control with moving horizon state estimation using jmodelica. In 10th International Modelica Conference 2014, 2014.
R. Franke, E. Arnold, and H. Linke. HQP: a solver for nonlinearly constrained large-scale optimization. URL http://hqp.sourceforge.net/.
Rüdiger Franke, Manfred Rode, and Klaus Krüger. On-line optimization of drum boiler startup. 2003.
Christian Hartlep and Toivo Henningsson. NMPC Application using JModelica.org: Features and Performance. In 11th International Modelica Conference 2015, 2015.
B. Houska, H.J. Ferreau, and M. Diehl. ACADO Toolkit – An Open Source Framework for Automatic Control and Dynamic Optimization. Optimal Control Applications and Methods, 32(3):298–312, 2011.
Per-Ola Larsson, Francesco Casella, Fredrik Magnusson, Joel Andersson, Moritz Diehl, and Johan Å kesson. A framework for nonlinear model-predictive control using objectoriented
modeling with a case study in power plant start-up.
In Computer Aided Control System Design (CACSD), 2013 IEEE Conference on, 2013.
Tor Larsson. Moving Horizon Estimation in JModelica.org. Master’s Thesis ISRN LUTFD2/TFRT--5982--SE, Department of Automatic Control, Lund University, Sweden, 2015.
Björn Lennernäs. A CasADi based toolchain for JModelica. org. Master’s thesis, Department of Automatic Control, Lund University, Sweden, June 2013.
J.M. Maciejowski. Predictive Control with Constraints. Prentice-Hall, 2002.
Fredrik Magnusson and Johan Åkesson. Dynamic optimization in jmodelica.org. Processes, 3(2):471–496, 2015.
Andreas Wächter. Short tutorial: getting started with ipopt in 90 minutes. Combinatorial Scientific Computing (U. Naumann, O. Schenk, HD Simon, eds.), 34(56):118, 2009.
Andreas Wächter and Lorenz T Biegler. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical programming, 106(1):25–57, 2006.
