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Inversion of fuzzy neural networks for the reduction of noise in the control loop for automotive applications

Nentwig M., Mercorelli P.

Journal of Automation Mobile Robotics and Intelligent Systems

2009

Vol. 3, No. 3

83-89

A robust throttle valve control has been an attractive problem since throttle by wire systems were established in the mid-nineties. Control strategies often use a feed-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 modelling. 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.