onr:"swepub:oai:DiVA.org:liu-56157"
Search: onr:"swepub:oai:DiVA.org:liu-56157" > A Dual Gradient Pro...
- 1 of 1
- Previous record
- Next record
- To hitlist
A Dual Gradient Projection Quadratic Programming Algorithm Tailored for Mixed Integer Predictive Control
-
- Axehill, Daniel (author)
- Linköpings universitet,Reglerteknik,Tekniska högskolan
-
- Hansson, Anders (author)
- Linköpings universitet,Reglerteknik,Tekniska högskolan
- (creator_code:org_t)
- Linköping : Linköping University Electronic Press, 2008
- English 58 s.
- Series: LiTH-ISY-R, 1400-3902 ; 2833
- Reports (other academic/artistic)
Abstract
Subject headings
Close
- The objective of this work is to derive a Mixed Integer Quadratic Programming algorithm tailored for Model Predictive Control for hybrid systems. The Mixed Integer Quadratic Programming algorithm is built on the branch and bound method, where Quadratic Programming relaxations of the original problem are solved in the nodes of a binary search tree. The difference between these subproblems is often small and therefore it is interesting to be able to use a previous solution as a starting point in a new subproblem. This is referred to as a warm start of the solver. Because of its warm start properties, an algorithm that works similar to an active set method is desired. A drawback with classical active set methods is that they often require many iterations in order to find the active set in optimum. So-called gradient projection methods are known to be able to identify this active set very fast. In the algorithm presented in this report, an algorithm built on gradient projection and projection of a Newton search direction onto the feasible set is used. It is a variant of a previously presented algorithm by the authors and makes it straightforward to utilize the previous result, where it is shown how the Newton search direction for the dual MPC problem can be computed very efficiently using Riccati recursions. As in the previous work, this operation can be performed with linear computational complexity in the prediction horizon. Moreover, the gradient computation used in the gradient projection part of the algorithm is also tailored for the problem in order to decrase the computational complexity. Furthermore, is is shown how a Riccati recursion still can be useful in the case when the system of equations for the ordinary search directino is inconsistent. In numerical experiments, the algorithm shows good performance, and it seems like the gradient projection strategy efficiently cuts down the number of Newton steps necessary to compute in order to reach the solution. When the algorithm is used as a part of an MIQP solver for hybrid MPC, the performance is still very good for small problems. However, for more difficult problems, there still seems to be some more work to do in order to get the performance of the commercial state-of-the-art solver CPLEX.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- Model predictive control
- Hybrid systems
- Quadratic programming
- Mixed integer quadratic programming
- TECHNOLOGY
- TEKNIKVETENSKAP
Publication and Content Type
- vet (subject category)
- rap (subject category)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Axehill, Daniel
- Hansson, Anders
- About the subject
-
- ENGINEERING AND TECHNOLOGY
- ENGINEERING AND ...
- and Electrical Engin ...
- and Control Engineer ...
- Parts in the series
- LiTH-ISY-R,
- By the university
- Linköping University
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
