Summary
The chapter deals with neural networks and learning machines for engine control applications, particularly in modeling for control. In the first section, basic features of engine control in a layered engine management architecture are reviewed. The use of neural networks for engine modeling, control and diagnosis is then briefly described. The need for descriptive models for model-based control and the link between physical models and black box models are emphasized by the grey box approach discussed in this chapter. The second section introduces the neural models frequently 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 also 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 demonstrates that models based on first principles and neural models must be joined together in a grey box approach to obtain effective and acceptable results.
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References
C. Alippi, C. De Russis, and V. Piuri. A neural-network based control solution to air fuel ratio for automotive fuel injection system. IEEE Transactions on Systems, Man, and Cybernetics – Part C, 33(2):259–268, 2003.
P. Andersson. Intake Air Dynamics on a Turbocharged SI Engine with Wastegate. PhD thesis, Linköping University, Sweden, 2002.
P. Andersson and L. Eriksson. Mean-value observer for a turbocharged SI engine. In Proc. of the IFAC Symp. on Advances in Automotive Control, Salerno, Italy, pages 146–151, April 2004.
I. Arsie, C. Pianese, and M. Sorrentino. A procedure to enhance identification of recurrent neural networks for simulating air-fuel ratio dynamics in SI engines. Engineering Applications of Artificial Intelligence, 19(1):65–77, 2006.
I. Arsie, C. Pianese, and M. Sorrentino. Recurrent neural networks for AFR estimation and control in spark ignition automotive engines. Chapter 9 in this volume.
A. R. Barron. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, 39(3):930–945, 1993.
G. Bloch and T. Denoeux. Neural networks for process control and optimization: two industrial applications. ISA Transactions, 42(1):39–51, 2003.
G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard. Combining experimental data and physical simulation models in support vector learning. In L. Iliadis and K. Margaritis, editors, Proc. of the 10th Int. Conf. on Engineering Applications of Neural Networks (EANN), Thessaloniki, Greece, volume 284 of CEUR Workshop Proceedings, pages 284–295, 2007.
G. Bloch, F. Sirou, V. Eustache, and P. Fatrez. Neural intelligent control of a steel plant. IEEE Transactions on Neural Networks, 8(4):910–918, 1997.
G. Colin. Contrôle des systèmes rapides non linéaires – Application au moteur à allumage commandé turbocompressé à distribution variable. PhD thesis, University of Orléans, France, 2006.
G. Colin, Y. Chamaillard, G. Bloch, and A. Charlet. Exact and linearised neural predictive control - a turbocharged SI engine example. Journal of Dynamic Systems, Measurement and Control - Transactions of the ASME, 129(4):527–533, 2007.
G. Colin, Y. Chamaillard, G. Bloch, and G. Corde. Neural control of fast nonlinear systems - Application to a turbocharged SI engine with VCT. IEEE Transactions on Neural Networks, 18(4):1101–1114, 2007.
G. Corde. Le contrôle moteur. In G. Gissinger and N. Le Fort Piat, editors, Contrôle commande de la voiture. Hermès, Paris, 2002.
J. Daafouz and J. Bernussou. Parameter dependent Lyapunov functions for discrete time systems with time varying parametric uncertainties. Systems & Control Letters, 43(5):355–359, August 2001.
G. De Nicolao, R. Scattolini, and C. Siviero. Modelling the volumetric efficiency of IC engines: parametric, non-parametric and neural techniques. Control Engineering Practice, 4(10):1405–1415, 1996.
P. M. L. Drezet and R. F. Harrison. Support vector machines for system identification. In Proc. of the UKACC Int. Conf. on Control, Swansea, UK, volume 1, pages 688–692, 1998.
J. W. Fox, W. K. Cheng, and J. B. Heywood. A model for predicting residual gas fraction in spark-ignition engines. SAE Technical Papers, (931025), 1993.
J. Gerhardt, H. Hönniger, and H. Bischof. A new approach to functionnal and software structure for engine management systems - BOSCH ME7. SAE Technical Papers, (980801), 1998.
P. Giansetti, G. Colin, P. Higelin, and Y. Chamaillard. Residual gas fraction measurement and computation. International Journal of Engine Research, 8(4):347–364, 2007.
L. Guzzella and C. H. Onder. Introduction to Modeling and Control of Internal Combustion Engine Systems. Springer, Berlin Heidelberg New York, 2004.
E. Hendricks and J. Luther. Model and observer based control of internal combustion engines. In Proc. of the 1st Int. Workshop on Modeling Emissions and Control in Automotive Engines (MECA), Salerno, Italy, pages 9–20, 2001.
Imagine. Amesim web site. www.amesim.com, 2006.
M. Jankovic and S. W. Magner. Variable Cam Timing : Consequences to automotive engine control design. In Proc. of the 15th Triennial IFAC World Congress, Barcelona, Spain, pages 271–276, 2002.
I. Kolmanovsky. Support vector machine-based determination of gasoline direct-injected engine admissible operating envelope. SAE Technical Papers, (2002-01-1301), 2002.
M. Lairi and G. Bloch. A neural network with minimal structure for maglev system modeling and control. In Proc. of the IEEE Int. Symp. on Intelligent Control/Intelligent Systems & Semiotics, Cambridge, MA, USA, pages 40–45, 1999.
F. Lauer and G. Bloch. Incorporating prior knowledge in support vector regression. Machine Learning, 70(1):89–118, January 2008.
F. Le Berr, M. Miche, G. Colin, G. Le Solliec, and F. Lafossas. Modelling of a turbocharged SI engine with variable camshaft timing for engine control purposes. SAE Technical Paper, (2006-01-3264), 2006.
B. Lecointe and G. Monnier. Downsizing a gasoline engine using turbocharging with direct injection. SAE Technical Paper, (2003-01-0542), 2003.
L. Ljung. System Identification: Theory for the User, 2nd edition. Prentice-Hall, Englewood Cliffs, NJ, 1999.
O. Mangasarian. Generalized support vector machines. In A. Smola, P. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 135–146. MIT, Cambridge, MA, 2000.
O. L. Mangasarian and D. R. Musicant. Large scale kernel regression via linear programming. Machine Learning, 46(1–3):255–269, 2002.
K. A. Marko. Neural network application to diagnostics and control of vehicle control systems. In R. Lippmann, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems, volume 3, pages 537–543. Morgan Kaufmann, San Mateo, CA, 1991.
D. Mattera and S. Haykin. Support vector machines for dynamic reconstruction of a chaotic system. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods: Support Vector Learning, pages 211–241. MIT, Cambridge, MA, 1999.
G. Millérioux, F. Anstett, and G. Bloch. Considering the attractor structure of chaotic maps for observer-based synchronization problems. Mathematics and Computers in Simulation, 68(1):67–85, February 2005.
G. Millérioux, L. Rosier, G. Bloch, and J. Daafouz. Bounded state reconstruction error for LPV systems with estimated parameters. IEEE Transactions on Automatic Control, 49(8):1385–1389, August 2004.
O. Nelles. Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin Heidelberg New York, 2001.
M. J. L. Orr. Recent advances in radial basis function networks. Technical report, Edinburgh University, UK, 1999.
T. Poggio and F. Girosi. Networks for approximation and learning. Proceedings of IEEE, 78(10):1481–1497, 1990.
D. V. Prokhorov. Neural networks in automotive applications. Chapter 7 in this volume.
G. V. Puskorius and L. A. Feldkamp. Parameter-based Kalman filter training: theory and implementation. In S. Haykin, editor, Kalman Filtering and Neural Networks, chapter 2, pages 23–67. Wiley, New York, 2001.
A. Rakotomamonjy, R. Le Riche, D. Gualandris, and Z. Harchaoui. A comparison of statistical learning approaches for engine torque estimation. Control Engineering Practice, 16(1):43–55, 2008.
R. Reed. Pruning algorithms – a survey. IEEE Transactions on Neural Networks, 4:740–747, 1993.
M. Rychetsky, S. Ortmann, and M. Glesner. Support vector approaches for engine knock detection. In Proc. of the Int. Joint Conf. on Neural Networks (IJCNN), Washington, DC, USA, volume 2, pages 969–974, 1999.
B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT, Cambridge, MA, 2001.
P. J. Shayler, M. S. Goodman, and T. Ma. The exploitation of neural networks in automotive engine management systems. Engineering Applications of Artificial Intelligence, 13(2):147–157, 2000.
J. Sjöberg and L. S. H. Ngia. Neural nets and related model structures for nonlinear system identification. In J. A. K. Suykens and J. Vandewalle, editors, Nonlinear Modeling, Advanced Black-Box Techniques, chapter 1, pages 1–28. Kluwer Academic Publishers, Boston, 1998.
J. Sjöberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P. Y. Glorennec, H. Hjalmarsson, and A. Juditsky. Nonlinear black-box modeling in system identification: a unified overview. Automatica, 31(12):1691–1724, 1995.
A. J. Smola and B. Schölkopf. A tutorial on support vector regression. Statistics and Computing, 14(3):199–222, 2004.
A. J. Smola, B. Schölkopf, and G. Rätsch. Linear programs for automatic accuracy control in regression. In Proc. of the 9th Int. Conf. on Artificial Neural Networks, Edinburgh, UK, volume 2, pages 575–580, 1999.
A. Stotsky and I. Kolmanovsky. Application of input estimation techniques to charge estimation and control in automotive engines. Control Engineering Practice, 10:1371–1383, 2002.
P. Thomas and G. Bloch. Robust pruning for multilayer perceptrons. In P. Borne, M. Ksouri, and A. El Kamel, editors, Proc. of the IMACS/IEEE Multiconf. on Computational Engineering in Systems Applications, Nabeul-Hammamet, Tunisia, volume 4, pages 17–22, 1998.
V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, Berlin Heidelberg New York, 1995.
C.-M. Vong, P.-K. Wong, and Y.-P. Li. Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference. Engineering Applications of Artificial Intelligence, 19(3):277–287, 2006.
L. Zhang and Y. Xi. Nonlinear system identification based on an improved support vector regression estimator. In Proc. of the Int. Symp. on Neural Networks, Dalian, China, volume 3173 of LNCS, pages 586–591. Springer, Berlin Heidelberg New York, 2004.
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Bloch, G., Lauer, F., Colin, G. (2008). On Learning Machines for Engine Control. In: Prokhorov, D. (eds) Computational Intelligence in Automotive Applications. Studies in Computational Intelligence, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79257-4_8
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DOI: https://doi.org/10.1007/978-3-540-79257-4_8
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