Solving equation systems associated with nonlinear model predictive control
(2015) In Bachelor's Theses in Mathematical Sciences NUMK01 20151Mathematics (Faculty of Engineering)
 Abstract
 When solving optimization problems associated with nonlinear model predictive control, a linear equation system of a specific structure frequently arises. Two different software, CVXGEN and FORCES, developed to generate tailored solvers to specific optimization problems use two different methods when solving this equation system. In this paper, two different sets of software is presented which generate tailored solvers for this equation system using the two different methods. It is found that the solvers which implement the method used in FORCES are faster than the solvers which implement the method used in CVXGEN, as are the generations of the solvers using the FORCES method.
A third software which generate solvers to optimization... (More)  When solving optimization problems associated with nonlinear model predictive control, a linear equation system of a specific structure frequently arises. Two different software, CVXGEN and FORCES, developed to generate tailored solvers to specific optimization problems use two different methods when solving this equation system. In this paper, two different sets of software is presented which generate tailored solvers for this equation system using the two different methods. It is found that the solvers which implement the method used in FORCES are faster than the solvers which implement the method used in CVXGEN, as are the generations of the solvers using the FORCES method.
A third software which generate solvers to optimization problems is QPgen, which is now not able to generate solvers for nonlinear model predictive control problems. The software presented in this paper is intended to be incorporated in the QPgen software in order to make it able to generate solvers for these problems as well. (Less)  Popular Abstract (Swedish)
 När olika fysiska processer ska regleras automatiskt finns det flera metoder att välja bland. En av dessa är så kallad optimeringsbaserad reglering. För att kunna använda optimeringsbaserad reglering krävs att en viss ekvation löses och för att kunna reglera processen snabbt, måste också ekvationen lösas snabbt.
I det här arbetet jämförs två olika metoder att lösa den nämnda ekvationen. De två metoderna förekommer i mjukvarorna CVXGEN och FORCES och vi kan se att metoden som används i FORCES är betydligt snabbare än metoden som används i CVXGEN. Mjukvara för att automatiskt generera program som löser ekvationen med de två metoderna presenteras också och planeras att ingå i ytterligare en annan programvara, QPgen.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/8056004
 author
 Ackzell, Erik ^{LU}
 supervisor

 Pontus Giselsson ^{LU}
 organization
 course
 NUMK01 20151
 year
 2015
 type
 M2  Bachelor Degree
 subject
 keywords
 Numerical linear algebra, model predictive control, automatic code generation, CVXGEN, FORCES
 publication/series
 Bachelor's Theses in Mathematical Sciences
 report number
 LUNFNA40072015
 ISSN
 16546229
 other publication id
 2015:K17
 language
 English
 id
 8056004
 date added to LUP
 20151116 12:43:45
 date last changed
 20151214 13:32:13
@misc{8056004, abstract = {When solving optimization problems associated with nonlinear model predictive control, a linear equation system of a specific structure frequently arises. Two different software, CVXGEN and FORCES, developed to generate tailored solvers to specific optimization problems use two different methods when solving this equation system. In this paper, two different sets of software is presented which generate tailored solvers for this equation system using the two different methods. It is found that the solvers which implement the method used in FORCES are faster than the solvers which implement the method used in CVXGEN, as are the generations of the solvers using the FORCES method. A third software which generate solvers to optimization problems is QPgen, which is now not able to generate solvers for nonlinear model predictive control problems. The software presented in this paper is intended to be incorporated in the QPgen software in order to make it able to generate solvers for these problems as well.}, author = {Ackzell, Erik}, issn = {16546229}, keyword = {Numerical linear algebra,model predictive control,automatic code generation,CVXGEN,FORCES}, language = {eng}, note = {Student Paper}, series = {Bachelor's Theses in Mathematical Sciences}, title = {Solving equation systems associated with nonlinear model predictive control}, year = {2015}, }