Inversion of Fuzzy Neural Networks for the Reduction of Noise in the Control Loop for Automotive Applications
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Inversion of Fuzzy Neural Networks for the Reduction of Noise in the Control Loop for Automotive Applications. / Nentwig, M.; Mercorelli, Paolo.
In: JAMRIS, Journal of Automation, Mobile Robotics & Intelligent Systems, Vol. 3, No. 3, 2009, p. 83-89.Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Inversion of Fuzzy Neural Networks for the Reduction of Noise in the Control Loop for Automotive Applications
AU - Nentwig, M.
AU - Mercorelli, Paolo
PY - 2009
Y1 - 2009
N2 - A robust throttle valve control has been an attractive problem since throttle bywire systems were established in the mid-nineties. Control strategies often use afeed-forward controller which use an inverse model; however, mathematical model inversions imply a high order of differentiation of the state variables resulting in noise effects. In general, neural networks are a very effective and popular tool for modeling. The inversion of a neural network makes it possible to use these networks in control problem schemes. This paper presents a control strategy based upon an inversion of a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented, and two control loops are explicitly compared.
AB - A robust throttle valve control has been an attractive problem since throttle bywire systems were established in the mid-nineties. Control strategies often use afeed-forward controller which use an inverse model; however, mathematical model inversions imply a high order of differentiation of the state variables resulting in noise effects. In general, neural networks are a very effective and popular tool for modeling. The inversion of a neural network makes it possible to use these networks in control problem schemes. This paper presents a control strategy based upon an inversion of a feed-forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented, and two control loops are explicitly compared.
KW - Engineering
M3 - Journal articles
VL - 3
SP - 83
EP - 89
JO - JAMRIS, Journal of Automation, Mobile Robotics & Intelligent Systems
JF - JAMRIS, Journal of Automation, Mobile Robotics & Intelligent Systems
SN - 1897-8649
IS - 3
ER -