Fast neural networks for diesel engine control design

被引：92

作者：

Hafner, M ^{[1
]}

Schüler, M ^{[1
]}

Nelles, O ^{[1
]}

Isermann, R ^{[1
]}

机构：

[1] Tech Univ Darmstadt, Lab Control Engn & Proc Automat, Inst Automat Control, D-64283 Darmstadt, Germany

来源：

CONTROL ENGINEERING PRACTICE | 2000年 / 8卷 / 11期

关键词：

engine control; nonlinear dynamic modeling; neural networks; exhaust optimization; fuzzy classification; real-time simulation;

D O I：

10.1016/S0967-0661(00)00057-5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Advanced engine control systems require accurate dynamic models of the combustion process, which are substantially nonlinear. This contribution presents the application of fast neural net models for engine control design purposes. After briefly introducing a special local linear radial basis function network (LOLIMOT) the process of building adequate dynamic engine models is discussed in detail. These neuro-models are then integrated into an upper-level emission optimization tool which calculates a cost function for exhaust versus consumption/torque and determines optimal engine settings. A DSP-based process computer system allows a fast application of the optimization tool at the engine test stand. (C) 2000 Elsevier Science Ltd. All rights reserved.

引用

页码：1211 / 1221

页数：11

共 24 条

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