The exploitation of neural networks in automotive engine management systems

Shayler, P. J.; Goodman, Michael S.; Ma, Tom

doi:10.1016/s0952-1976(99)00048-2

Cited by 36 publications

(12 citation statements)

References 11 publications

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“…In [38] The works are too numerous to be referenced here. Nevertheless, the reader can consult the recent articles [43,1,4] and the references therein, for an overview.…”

Section: Neural Network In Engine Controlmentioning

confidence: 99%

On Learning Machines for Engine Control

Bloch

Lauer

Colin

2008

Studies in Computational Intelligence

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Summary. The chapter deals with neural networks and learning machines for engine control applications, particularly in modeling for control. In the first section, some basics on the common features of engine control are recalled, based on a layered engine management structure. Then the use of neural networks for engine modeling, control and diagnosis is briefly described. The need for descriptive models for model-based control and the link between physical models and black box models are emphasized at the end of this section by exposing the grey box approach taken in this chapter. The second section introduces the neural models most used in engine control, namely, MultiLayer Perceptrons (MLP) and Radial Basis Function (RBF) networks. A more recent approach, known as Support Vector Regression (SVR), to build models in kernel expansion form is then presented. The third section is devoted to examples of application of these models in the context of turbocharged Spark Ignition (SI) engines with Variable Camshaft Timing (VCT). This specific context is representative of modern engine control problems. In the first example, the airpath control is studied, where open loop neural estimators are combined with a dynamical polytopic observer. The second example considers modeling the in-cylinder residual gas fraction by Linear Programming SVR (LP-SVR), based on a limited amount of experimental data and a simulator built from prior knowledge. Each example tries to show that models based on first principles and neural models must be joined together in a grey box approach to obtain efficient and acceptable results.

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“…In [38] The works are too numerous to be referenced here. Nevertheless, the reader can consult the recent articles [43,1,4] and the references therein, for an overview.…”

Section: Neural Network In Engine Controlmentioning

confidence: 99%

On Learning Machines for Engine Control

Bloch

Lauer

Colin

2008

Studies in Computational Intelligence

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“…Applications range from control [25], prediction [26][27][28], modelling [29][30][31], fault diagnosis [32], feature extraction [33,34] to engine management [35] and data analysis [36].…”

Section: Introductionmentioning

confidence: 99%

Neuro semantic thresholding using OCR software for high precision OCR applications

Lázaro

Martín

Arias

et al. 2010

Image and Vision Computing

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“…The effectiveness of a non-linear principal component analysis based (PCA-based) monitoring scheme was illustrated for drifting fault in the fuel flow sensor due to a partial blockage of the intercooler in a Volkswagen TDI 1.9 litre diesel engine in [11]. The pattern recognition and classification abilities of networks were applied to crankshaft speed fluctuation data for engine-fault diagnosis, and multidimensional mapping capabilities were investigated as an alternative to large lookup-tables and calibration functions in [12]. A continuous wavelet transform technique for the fault signal diagnosis in an internal combustion (IC) engine and its cooling system was presented in [13].…”

Section: Introductionmentioning

confidence: 99%

Robustness assessment and adaptive FDI for car engine

Sangha

Gomm

2008

Int. J. Autom. Comput.

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A new on-line fault detection and isolation (FDI) scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and can be directly implemented in an on-board diagnosis system (hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model (MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

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The exploitation of neural networks in automotive engine management systems

Cited by 36 publications

References 11 publications

On Learning Machines for Engine Control

On Learning Machines for Engine Control

Neuro semantic thresholding using OCR software for high precision OCR applications

Robustness assessment and adaptive FDI for car engine

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