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Sequential alternating least squares for solving high dimensional linear Hamilton-Jacobi-Bellman equation
KTH.
2016 (English)In: IEEE International Conference on Intelligent Robots and Systems, IEEE, 2016, 3757-3764 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a technique to efficiently solve the Hamilton-Jacobi-Bellman (HJB) equation for a class of stochastic affine nonlinear dynamical systems in high dimensions. The HJB solution provides a globally optimal controller to the associated dynamical system. However, the curse of dimensionality, commonly found in robotic systems, prevents one from solving the HJB equation naively. This work avoids the curse by representing the linear HJB equation using tensor decomposition. An alternating least squares (ALS) based technique finds an approximate solution to the linear HJB equation. A straightforward implementation of the ALS algorithm results in ill-conditioned matrices that prevent approximation to a high order of accuracy. This work resolves the ill-conditioning issue by computing the solution sequentially and introducing boundary condition rescaling. Both of these additions reduce the condition number of matrices in the ALS-based algorithm. A MATLAB tool, Sequential Alternating Least Squares (SeALS), that implements the new method is developed. The performance of SeALS is illustrated using three engineering examples: an inverted pendulum, a Vertical Takeoff and Landing aircraft, and a quadcopter with state up to twelve.

Place, publisher, year, edition, pages
IEEE, 2016. 3757-3764 p.
Keyword [en]
Approximation algorithms, Dynamic programming, Dynamical systems, Intelligent robots, Jacobian matrices, MATLAB, Matrix algebra, Nonlinear dynamical systems, Nonlinear equations, Number theory, Stochastic systems, Alternating least squares, Approximate solution, Curse of dimensionality, Hamilton Jacobi Bellman equation, Hamilton-Jacobi-Bellman equations, Ill-conditioned matrices, Optimal controller, Tensor decomposition, Least squares approximations
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-202120DOI: 10.1109/IROS.2016.7759553ISI: 000391921703116Scopus ID: 2-s2.0-85006355856ISBN: 9781509037629 (print)OAI: oai:DiVA.org:kth-202120DiVA: diva2:1077672
Conference
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, 9 October 2016 through 14 October 2016
Note

QC 20170228

Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2017-03-06Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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