NDLI: Dynamic neural network methods applied to on-vehicle idle speed control

Content Provider	IEEE Xplore Digital Library
Author	Puskorius, G.V. Feldkamp, L.A. Davis Jr., L.I.
Copyright Year	1963
Abstract	The application of neural network techniques to the control of nonlinear dynamical systems has been the subject of substantial interest and research in recent years. In our own work, we have concentrated on extending the dynamic gradient formalism as established by Narendra and Parthasarathy (1990, 1991), and on employing it for applications in the control of nonlinear systems, with specific emphasis on automotive subsystems. The results we have reported to date, however, have been based exclusively upon simulation studies. In this paper, we establish that dynamic gradient training methods can be successfully used for synthesizing neural network controllers directly on instances of real systems. In particular we describe the application of dynamic gradient methods for training a time-lagged recurrent neural network feedback controller for the problem of engine idle speed control on an actual vehicle, discuss hardware and software issues, and provide representative experimental results.
Sponsorship	IEEE Publication
Starting Page	1407
Ending Page	1420
Page Count	14
File Size	1739869
File Format	PDF
ISSN	00189219
Volume Number	84
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Vehicle dynamics Nonlinear control systems Control systems Nonlinear dynamical systems Nonlinear systems Automotive engineering Control system synthesis Network synthesis Application software
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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