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Distributed Model Predictive Control for Building Temperature Control

Carstaedt, Kirsten (2016) In MSc Theses
Department of Automatic Control
Abstract
This thesis and technical report concentrates on distributed control using a distributed model predictive scheme. The model of a two room house and three room houses is build and a distributed model predictive control (MPC) algorithm is implemented in order to reach specified room temperatures with minimized energy effort in each room. For reference tracking Target Calculation and the delta input scheme are used. The MPC optimization problem is solved at each time step through an iterative method, where the number of iterations is reduced through a stopping criterion guaranteeing stability and a prespecified amount of performance and feasibility. The optimization problem is divided up into subproblems, where each subproblem takes less... (More)
This thesis and technical report concentrates on distributed control using a distributed model predictive scheme. The model of a two room house and three room houses is build and a distributed model predictive control (MPC) algorithm is implemented in order to reach specified room temperatures with minimized energy effort in each room. For reference tracking Target Calculation and the delta input scheme are used. The MPC optimization problem is solved at each time step through an iterative method, where the number of iterations is reduced through a stopping criterion guaranteeing stability and a prespecified amount of performance and feasibility. The optimization problem is divided up into subproblems, where each subproblem takes less computational effort than the central optimization problem. Due to the possibility of coupling between subsystems, communication between the subsystems is needed. The reference values are reached and iterations needed to solve the optimization are reduced with the stopping condition. This method saves computing time and gives privacy to each subsystem, since only required information is communicated. Also the subsystems get less susceptible to the failure of one coupled subsystem, since if one subsystem fails, the others could go on. But, due to the needed communication, t h i s method is more suitable for large systems with sparse coupling. For a small system, or too much coupling the communication effort will get to high. (Less)
Please use this url to cite or link to this publication:
author
Carstaedt, Kirsten
supervisor
organization
year
type
H3 - Professional qualifications (4 Years - )
subject
publication/series
MSc Theses
report number
TFRT-5998
ISSN
0280-5316
language
English
id
8775922
date added to LUP
2016-02-28 16:54:14
date last changed
2016-02-28 16:54:14
@misc{8775922,
  abstract     = {This thesis and technical report concentrates on distributed control using a distributed model predictive scheme. The model of a two room house and three room houses is build and a distributed model predictive control (MPC) algorithm is implemented in order to reach specified room temperatures with minimized energy effort in each room. For reference tracking Target Calculation and the delta input scheme are used. The MPC optimization problem is solved at each time step through an iterative method, where the number of iterations is reduced through a stopping criterion guaranteeing stability and a prespecified amount of performance and feasibility. The optimization problem is divided up into subproblems, where each subproblem takes less computational effort than the central optimization problem. Due to the possibility of coupling between subsystems, communication between the subsystems is needed. The reference values are reached and iterations needed to solve the optimization are reduced with the stopping condition. This method saves computing time and gives privacy to each subsystem, since only required information is communicated. Also the subsystems get less susceptible to the failure of one coupled subsystem, since if one subsystem fails, the others could go on. But, due to the needed communication, t h i s method is more suitable for large systems with sparse coupling. For a small system, or too much coupling the communication effort will get to high.},
  author       = {Carstaedt, Kirsten},
  issn         = {0280-5316},
  language     = {eng},
  note         = {Student Paper},
  series       = {MSc Theses},
  title        = {Distributed Model Predictive Control for Building Temperature Control},
  year         = {2016},
}