NDLI: Trajectory generation in guided spaces using NTG algorithm and artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Muezzinoglu, M.K. Inanc, T.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Louisville Univ., KY (Muezzinoglu, M.K.; Inanc, T.)
Abstract	This paper presents the preliminary results of nonlinear trajectory generation (NTG) using artificial neural networks (ANNs) as analytical data approximators. NTG framework designed at Caltech by Mark Milam et al. (2003) solves constrained nonlinear dynamic optimization problems in real time. A successful application of NTG on real-life problems with sampled data depends upon an accurate approximation scheme. Such an approximator is desired to have a compact architecture, a minimum number of design parameters, and a smooth continuously-differentiable input/output mapping. ANNs as universal approximators are known to possess these features, thus considered here as appropriate candidates for this task. The proposed cooperation of NTG and ANN is illustrated on an optimal control problem of generating realtime low observable trajectories for unmanned air vehicles in the presence of multiple radars
Sponsorship	American Automatic Control Council IFAC
File Size	638593
File Format	PDF
ISBN	1424402093
e-ISBN	1424402107
DOI	10.1109/ACC.2006.1657646
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Intelligent networks Artificial neural networks Optimal control Polynomials Spline Constraint optimization Motion planning Computer architecture Data analysis Vehicle dynamics
Content Type	Text
Resource Type	Article

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