Open AccessAn artificial neural network controller for intelligent transportation systems applications
Author(s) -
Javier E. Vitela,
Ulf R. Hanebutte,
Jaques Reifman
Publication year - 1996
Publication title -
osti oai (u.s. department of energy office of scientific and technical information)
Language(s) - English
Resource type - Reports
DOI - 10.2172/219376
Subject(s) - artificial neural network , cruise control , controller (irrigation) , intelligent transportation system , acceleration , feed forward , control engineering , intelligent control , computer science , simulation , nonlinear system , feedforward neural network , control system , vehicle dynamics , engineering , artificial intelligence , control (management) , automotive engineering , civil engineering , physics , electrical engineering , classical mechanics , quantum mechanics , agronomy , biology
An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work
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