Local basis function networks for identification of a turbocharger

This paper deals with nonlinear dynamic system identification by local basis function networks. A special kind of local basis function network generated by a tree construction algorithm is proposed. This local linear model tree (LOLIMOT) is applied for identification of a truck diesel engine exhaust turbocharger. The charging pressure is modelled as the output of a nonlinear second order multiple input system with engine speed and injection rate as inputs. The LOLIMOT approach was capable to identify the turbocharger with measured signals during road driving and with ten local linear models in less than one minute on a Pentium PC.