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Practical comparison of MPC Toolboxes

Nilsson, Emma (2021)
Department of Automatic Control
Abstract
Model predictive control can be used to control a range of processes, from selfdriving cars to chemical plants. The education of the engineering students that in the future will design these controllers is an important matter. In this thesis, three
Matlab toolboxes have been evaluated and compared from a student’s perspective.
The toolboxes are Mathwork’s own toolbox called Model Predictive Control Toolbox, Multi-Parametric Toolbox 3, and MATMPC. With a practical approach, three types of controllers have been created in each toolbox, a basic MPC controller, an MPC controller with integral action, and an MPC controller with gain scheduling. The documentation has been explored and the notation has been compared with the theory that is... (More)
Model predictive control can be used to control a range of processes, from selfdriving cars to chemical plants. The education of the engineering students that in the future will design these controllers is an important matter. In this thesis, three
Matlab toolboxes have been evaluated and compared from a student’s perspective.
The toolboxes are Mathwork’s own toolbox called Model Predictive Control Toolbox, Multi-Parametric Toolbox 3, and MATMPC. With a practical approach, three types of controllers have been created in each toolbox, a basic MPC controller, an MPC controller with integral action, and an MPC controller with gain scheduling. The documentation has been explored and the notation has been compared with the theory that is taught to the students. Different ways to implement integral action and gain scheduling have been evaluated and the controllers have been run with a real process to emulate a laboratory session and the results from the different toolboxes have been compared.
The notation in Multi-Parametric Toolbox 3 did best correspond to the students’ knowledge about MPC and had in addition the best performance of the three toolboxes. (Less)
Please use this url to cite or link to this publication:
author
Nilsson, Emma
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
report number
TFRT-6124
other publication id
0280-5316
language
English
id
9042120
date added to LUP
2021-03-18 14:27:15
date last changed
2021-03-18 14:27:15
@misc{9042120,
  abstract     = {{Model predictive control can be used to control a range of processes, from selfdriving cars to chemical plants. The education of the engineering students that in the future will design these controllers is an important matter. In this thesis, three
Matlab toolboxes have been evaluated and compared from a student’s perspective.
 The toolboxes are Mathwork’s own toolbox called Model Predictive Control Toolbox, Multi-Parametric Toolbox 3, and MATMPC. With a practical approach, three types of controllers have been created in each toolbox, a basic MPC controller, an MPC controller with integral action, and an MPC controller with gain scheduling. The documentation has been explored and the notation has been compared with the theory that is taught to the students. Different ways to implement integral action and gain scheduling have been evaluated and the controllers have been run with a real process to emulate a laboratory session and the results from the different toolboxes have been compared.
 The notation in Multi-Parametric Toolbox 3 did best correspond to the students’ knowledge about MPC and had in addition the best performance of the three toolboxes.}},
  author       = {{Nilsson, Emma}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Practical comparison of MPC Toolboxes}},
  year         = {{2021}},
}