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On the use of gradual dense-sparse discretizations in receding horizon control
FOI.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-7714-928X
2014 (English)In: Optimal control applications & methods, ISSN 0143-2087, E-ISSN 1099-1514, Vol. 35, no 3, 253-270 p.Article in journal (Refereed) Published
Abstract [en]

A key factor to success in implementations of real time optimal control, such as receding horizon control (RHC), is making efficient use of computational resources. The main trade-off is then between efficiency and accuracy of each RHC iteration, and the resulting overall optimality properties of the concatenated iterations, that is, how closely this represents a solution to the underlying infinite time optimal control problem (OCP). Both these issues can be addressed by adapting the RHC solution strategy to the expected form of the solution. Using gradual dense-sparse (GDS) node distributions in direct transcription formulations of the finite time OCP solved in each RHC iteration is a way of adapting the node distribution of this OCP to the fact that it is actually part of an RHC scheme. We have previously argued that this is reasonable, because the near future plan must be implemented now, but the far future plan can and will be revised later. In this paper, we investigate RHC applications where the asymptotic qualitative behavior of the OCP solution can be analyzed in advance. For some classes of systems, explicit exponential convergence rates of the solutions can be computed. We establish such convergence rates for a class of control affine nonlinear systems with a locally quadratic cost and propose to use versions of GDS node distributions for such systems because they will (eventually) be better adapted to the form of the solution. The advantages of the GDS approach in such settings is illustrated with simulations.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2014. Vol. 35, no 3, 253-270 p.
Keyword [en]
numerical solution, receding horizon control, convergence, optimal control
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-124009DOI: 10.1002/oca.2065ISI: 000334781900001Scopus ID: 2-s2.0-84899477013OAI: oai:DiVA.org:kth-124009DiVA: diva2:632192
Note

QC 20130710

Available from: 2013-06-24 Created: 2013-06-24 Last updated: 2017-12-06Bibliographically approved

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Ögren, Petter

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Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
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