This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Virtual Sensing of SI Engines Using Recurrent Neural Networks
Technical Paper
2006-01-1348
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
For engine diagnostics and fault-tolerant control system design provision of analytical models, in the form of virtual sensors, will enable more reliable system design and operation. This paper presents applications of recurrent neural network (RNN)-based architectures for the development of virtual sensors for salient SI engine variables such as manifold absolute pressure, mass airflow rate, air-fuel ratio and engine torque. The RNN architectures developed allow effective sensing of these crucial engine variables while, for computational efficiency, keeping a compact size for the network topology. A nonlinear state-space model strategy is proposed for architecting the stated recurrent neural network and is trained using variants of the real-time recurrent learning (RTRL) algorithm. Representative experimental results obtained for a 5.7 L V8 engine are listed and discussed. The application, dependency and limitations of the proposed approaches are also pointed out.
Recommended Content
Authors
Topic
Citation
Kamat, S., Diwanji, V., Smith, J., Javaherian, H. et al., "Virtual Sensing of SI Engines Using Recurrent Neural Networks," SAE Technical Paper 2006-01-1348, 2006, https://doi.org/10.4271/2006-01-1348.Also In
References
- Blomqvist, D. Byttner S. Holmberg Ulf Rögnvaldsson T. “Different Strategies for Transient Control of the Air-Fuel Ratio in a SI Engine” SAE, Publication no. 2000-01-2835
- Onder, C.H. Geering H.P. “Model Based Multivariable Speed and Air-Fuel Ratio Control of a SI Engine” SAE Publication no. 930859
- Simons, M.R. Locatelli M. Onder C.H. Geering H.P. “A Non-linear Wall-Wetting Model for the Complete Operating Region of a Sequential Fuel Injected SI Engine” SAE Publication no. 2000-01-1260
- Williams, R. J. Zipser D. “A learning Algorithm for Continually Running Fully Recurrent Neural Networks” Neural Computation 1 270 280 1989
- Haykin Simon “Neural Networks- A Comprehensive Foundation” Pearson Education 1999
- Narendra, K.S. Parthasarathy K. “Identification and Control of Dynamical Systems using Neural Networks” IEEE Trans. on Neural Networks 1 4 27 1990
- Lenz, Ulrich Schröder Dierk “Air-Fuel Ratio Control for Direct Injecting Combustion Engines using Neural Networks” SAE Publication no. 981060
- Müller, Rainer Schneider Bernd “Approximation and Control of the Engine Torque using Neural Networks” SAE Publication no. 2000-01-0929
- Javaherian, Hossein Liu Derong Zhang Yi Kovalenko Olesia “Adaptive Critic Learning Techniques for Automotive Engine Control” Proceeding of the 2004 American Control Conference Boston, Massachusetts 4066 4071 June 30 July 2 2004