NDLI: Information-theoretic stochastic optimal control via incremental sampling-based algorithms

Content Provider	IEEE Xplore Digital Library
Author	Arslan, O. Theodorou, E.A. Tsiotras, P.
Copyright Year	2014
Description	Author affiliation: Daniel Guggenheim Sch. of Aerosp. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Arslan, O.; Theodorou, E.A.; Tsiotras, P.)
Abstract	This paper considers optimal control of dynamical systems which are represented by nonlinear stochastic differential equations. It is well-known that the optimal control policy for this problem can be obtained as a function of a value function that satisfies a nonlinear partial differential equation, namely, the Hamilton-Jacobi-Bellman equation. This nonlinear PDE must be solved backwards in time, and this computation is intractable for large scale systems. Under certain assumptions, and after applying a logarithmic transformation, an alternative characterization of the optimal policy can be given in terms of a path integral. Path Integral (PI) based control methods have recently been shown to provide elegant solutions to a broad class of stochastic optimal control problems. One of the implementation challenges with this formalism is the computation of the expectation of a cost functional over the trajectories of the unforced dynamics. Computing such expectation over trajectories that are sampled uniformly may induce numerical instabilities due to the exponentiation of the cost. Therefore, sampling of low-cost trajectories is essential for the practical implementation of PI-based methods. In this paper, we use incremental sampling-based algorithms to sample useful trajectories from the unforced system dynamics, and make a novel connection between Rapidly-exploring Random Trees (RRTs) and information-theoretic stochastic optimal control. We show the results from the numerical implementation of the proposed approach to several examples.
Starting Page	1
Ending Page	8
File Size	1329229
Page Count	8
File Format	PDF
ISBN	9781479945528
DOI	10.1109/ADPRL.2014.7010617
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	stochastic optimal control sampling-based algorithms Heuristic algorithms Noise path integral Optimal control Aerospace electronics Entropy Trajectory Equations
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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