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Controlling the level of sparsity in MPC
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-6957-2603
2015 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 76, 1-7 p.Article in journal (Refereed) Published
Abstract [en]

In optimization algorithms used for on-line Model Predictive Control (MPC), linear systems of equations are often solved in each iteration. This is true both for Active Set methods as well as for Interior Point methods, and for linear MPC as well as for nonlinear MPC and hybrid MPC. The main computational effort is spent while solving these linear systems of equations, and hence, it is of greatest interest to solve them efficiently. Classically, the optimization problem has been formulated in either of two ways. One leading to a sparse linear system of equations involving relatively many variables to compute in each iteration and another one leading to a dense linear system of equations involving relatively few variables. In this work, it is shown that it is possible not only to consider these two distinct choices of formulations. Instead it is shown that it is possible to create an entire family of formulations with different levels of sparsity and number of variables, and that this extra degree of freedom can be exploited to obtain even better performance with the software and hardware at hand. This result also provides a better answer to a recurring question in MPC; should the sparse or dense formulation be used.

Place, publisher, year, edition, pages
2015. Vol. 76, 1-7 p.
Keyword [en]
Predictive control, Optimization, Riccati recursion, Sparsity
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-113276DOI: 10.1016/j.sysconle.2014.12.002ISI: 000349733500001OAI: oai:DiVA.org:liu-113276DiVA: diva2:780367
Available from: 2015-01-14 Created: 2015-01-14 Last updated: 2017-12-05Bibliographically approved

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