FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control
- 1 July 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 10 (4) , 885-897
- https://doi.org/10.1109/72.774241
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.Keywords
This publication has 20 references indexed in Scilit:
- On Tikhonov regularization, bias and variance in nonlinear system identificationAutomatica, 1997
- Design and analysis of gain-scheduled control using local controller networksInternational Journal of Control, 1997
- Local controller network for autonomous vehicle steeringControl Engineering Practice, 1996
- DISCRETE-TIME NEURAL MODEL STRUCTURES FOR CONTINUOUS-TIME NONLINEAR SYSTEMS: FUNDAMENTAL PROPERTIES AND CONTROL ASPECTSPublished by World Scientific Pub Co Pte Ltd ,1996
- Constructive empirical modelling of longitudinal vehicle dynamics using local model networksControl Engineering Practice, 1996
- Nonlinear black-box modeling in system identification: a unified overviewAutomatica, 1995
- Identification Using Feedforward NetworksNeural Computation, 1995
- Constructing NARMAX models using ARMAX modelsInternational Journal of Control, 1993
- Neural networks for control systems—A surveyAutomatica, 1992
- A NARMAX model representation for adaptive control based on local modelsModeling, Identification and Control: A Norwegian Research Bulletin, 1992