Inversion of Fuzzy Neural Networks for the Reduction of Noise in the Control Loop for Automotive Applications

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 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.