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An Alternative use of the Riccati Recursion for Efficient Optimization
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-6957-2603
ETH Zürich, Switzerland. (Automatic Control Laboratory)
2012 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 61, no 1, 37-40 p.Article in journal (Refereed) Published
Abstract [en]

In optimization routines used for on-line Model Predictive Control (MPC), linear systems of equations are solved in each iteration. This is true both for Active Set (AS) solvers as well as for Interior Point (IP) solvers, 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 great interest to solve them efficiently. In high performance solvers for MPC, this is performed using Riccati recursions or generic sparsity exploiting algorithms. To be able to get this performance gain, the problem has to be formulated in a sparse way which introduces more variables. The alternative is to use a smaller formulation where the objective function Hessian is dense. In this work, it is shown that it is possible to exploit the structure also when using the dense formulation. More specifically, it is shown that it is possible to efficiently compute a standard Cholesky factorization for the dense formulation. This results in a computational complexity that grows quadratically in the prediction horizon length instead of cubically as for the generic Cholesky factorization.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 61, no 1, 37-40 p.
Keyword [en]
Predictive control, Optimization, Riccati recursion, Matrix factorization
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-73667DOI: 10.1016/j.sysconle.2011.09.018OAI: diva2:475656
Available from: 2012-01-11 Created: 2012-01-11 Last updated: 2016-08-31

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Axehill, Daniel
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