FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control

Abstract

An finite-element methods (FEM)-based neural-network approach to Nonlinear AutoRegressive with eXogenous input (NARX) modeling is presented. The method uses multilinear interpolation functions on C0 rectangular elements. The local and global structure of the resulting model is analyzed. It is shown that the model can be interpreted both as a local model network and a single layer feedforward neural network. The main aim is to use the model for nonlinear control design. The proposed FEM NARX description is easily accessible to feedback linearizing control techniques. Its use with a two-degrees of freedom nonlinear internal model controller is discussed. The approach is applied to modeling of the nonlinear longitudinal dynamics of an experimental lorry, using measured data. The modeling results are compared with local model network and multilayer perceptron approaches. A nonlinear speed controller was designed based on the identified FEM model. The controller was implemented in a test vehicle, and several experimental results are presented.