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Authors
Schreiber, Alexander; Isermann, Rolf

Identifier
10.3182/20070820-3-US-2918.00048

Index Terms
engine modeling,nonlinear models,neural networks,dynamic modeling,test generation

Abstract
The optimized control of combustion engines with regard to minimized fuel consumption and emissions requires nonlinear models. Because of an increase of control inputs, like fuel mass flow, injection angle, exhaust gas recirculation flow and several outputs like torque, nitrogen oxides (NOx), hydrocarbons (HC) and particulates the classical grid-based measurement techniques take too long time and do not include dynamics. Therefore different measurement strategies for the stationary and dynamic behavior are described, like Design of Experiments (DoE) and use of suitable neural networks and Pseudo-Random-Binary-Signals (PRBS). As the structure of the models is not precisely known a-priori, nonlinear identification methods in form of special versions of neural networks are good candidates. Therefore, it will be shown how with special amplitude-modulated pseudo random binary signals (APRBS), simultaneous excitation of several input signals, nonlinear multi-input multi-output models can be obtained in relatively short time.

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