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Nonlinear Model Predictive Control in JModelica.org

Axelsson, Magdalena (2015)
Department of Automatic Control
Abstract
In this thesis, a stronger support for Model Predictive Control (MPC) in JModelica.org has been implemented. JModelica.org is an open-source software for simulation and optimization of systems described by Modelica models. MPC is an optimization-based control strategy where one formulates an Optimal Control Problem (OCP) to describe the aim of the controller. At discrete time points the state of the system is estimated and the OCP is solved to find the optimal input to apply to the system. The main goal of this thesis has been to make the time it takes to obtain the optimal input as short as possible and also streamlining the setup of MPC in JModelica.org. This has been done by implementing an MPC class, which utilizes the fact that the... (More)
In this thesis, a stronger support for Model Predictive Control (MPC) in JModelica.org has been implemented. JModelica.org is an open-source software for simulation and optimization of systems described by Modelica models. MPC is an optimization-based control strategy where one formulates an Optimal Control Problem (OCP) to describe the aim of the controller. At discrete time points the state of the system is estimated and the OCP is solved to find the optimal input to apply to the system. The main goal of this thesis has been to make the time it takes to obtain the optimal input as short as possible and also streamlining the setup of MPC in JModelica.org. This has been done by implementing an MPC class, which utilizes the fact that the structure of the OCP is the same in each consecutive sample for efficiency. Two different benchmarks, one on a smaller problem and one on a larger problem, shows that by using the new MPC framework we obtain similar results as before, but considerably faster. The total average computation time for one sample is decreased by almost 60% for the large problem and by almost 90% for the smaller problem. (Less)
Please use this url to cite or link to this publication:
author
Axelsson, Magdalena
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
ISSN
0280-5316
other publication id
ISRN LUTFD2/TFRT--5987--SE
language
English
id
7760729
date added to LUP
2015-08-18 09:21:02
date last changed
2015-08-18 09:21:02
@misc{7760729,
  abstract     = {In this thesis, a stronger support for Model Predictive Control (MPC) in JModelica.org has been implemented. JModelica.org is an open-source software for simulation and optimization of systems described by Modelica models. MPC is an optimization-based control strategy where one formulates an Optimal Control Problem (OCP) to describe the aim of the controller. At discrete time points the state of the system is estimated and the OCP is solved to find the optimal input to apply to the system. The main goal of this thesis has been to make the time it takes to obtain the optimal input as short as possible and also streamlining the setup of MPC in JModelica.org. This has been done by implementing an MPC class, which utilizes the fact that the structure of the OCP is the same in each consecutive sample for efficiency. Two different benchmarks, one on a smaller problem and one on a larger problem, shows that by using the new MPC framework we obtain similar results as before, but considerably faster. The total average computation time for one sample is decreased by almost 60% for the large problem and by almost 90% for the smaller problem.},
  author       = {Axelsson, Magdalena},
  issn         = {0280-5316},
  language     = {eng},
  note         = {Student Paper},
  title        = {Nonlinear Model Predictive Control in JModelica.org},
  year         = {2015},
}