Adaptive Fuzzy Control for Air-Fuel Ratio of Automobile Spark Ignition Engine
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Adaptive Fuzzy Control for Air-Fuel Ratio of Automobile Spark Ignition Engine

Authors: Ali Ghaffari, A. Hosein Shamekhi, Akbar Saki, Ehsan Kamrani

Abstract:

In order to meet the limits imposed on automotive emissions, engine control systems are required to constrain air/fuel ratio (AFR) in a narrow band around the stoichiometric value, due to the strong decay of catalyst efficiency in case of rich or lean mixture. This paper presents a model of a sample spark ignition engine and demonstrates Simulink-s capabilities to model an internal combustion engine from the throttle to the crankshaft output. We used welldefined physical principles supplemented, where appropriate, with empirical relationships that describe the system-s dynamic behavior without introducing unnecessary complexity. We also presents a PID tuning method that uses an adaptive fuzzy system to model the relationship between the controller gains and the target output response, with the response specification set by desired percent overshoot and settling time. The adaptive fuzzy based input-output model is then used to tune on-line the PID gains for different response specifications. Experimental results demonstrate that better performance can be achieved with adaptive fuzzy tuning relative to similar alternative control strategies. The actual response specifications with adaptive fuzzy matched the desired response specifications.

Keywords: Modelling, Air–fuel ratio control, SI engine, Adaptive fuzzy Control.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1075256

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2524

References:


[1] Heywood, J. B. (1988). Internal Combustion Engine Fundumentals. New York: McGraw-Hill.
[2] Balluchi, A., Benvenuti, L., Di Benedetto, MD., Pinello, C., Sangiovanni- Vincentelli, AL., 2000. Automotive engine control and hybrid systems: challenges and opportunities. Proceedings of the IEEE 88 (7), 888-912.
[3] De Nicolao, G., Scattolini, R., Siviero, C., 1996. Modelling the volumetric efficiency of IC engines: parametric, non-parametric and neural techniques. Control Engineering Practice 4 (10), 1405-1415.
[4] Tan, Y., Mehrdad, S., 2000. Neural-networks-based nonlinear dynamic modeling for automotive engines. Neurocomputing 30, 129-142.
[5] Vinsonneau, J.A.F., Shields, D.N., King, P.J., Burnham, K.J., 2003. Polynomial and neural network spark ignition engine intake manifold modeling. Proceedings of the sixteenth International Conference on Systems Engineering, ICSE-, vol.2, pp. 718-723.
[6] Moskwa J. "Automotive Engine Modeling for Real Time Control" PhD thesis M.I.T,1998
[7] Manzie, C., Palaniswami, M., Watson, H., 2001. Gaussian networks for fuel injection control. Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering 215 (D10), 1053- 1068.
[8] Manzie, C., Palaniswami, M., Ralph, D., Watson, H., Yi, X., 2002. Model predictive control of a fuel injection system with a radial basis function network observer. Journal of Dynamic Systems Measurement and Control Transactions of the ASME 124 (4), 648-658.
[9] Choi. S.B., Hendrick, J.K., 1998. An observer-based controller design method for improving air/fuel characteristics of spark ignition engines. IEEE Transactions on Control Systems Technology 6 (3), 325-334.
[10] Yoon, R., Sunwoo, M., 2001. An adaptive sliding mode controller for airfuel ratio control of spark ignition engines. Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering 215, 305-315.
[11] K. Horie, K. Nishizawa, T. Ogawa, S. Akazaki, and K. Miura, "The Development of a High Fuel Economy and High Performance Four- Valve Lean Burn Engine", SAE Paper No. 920455, 1992.
[12] T. Inoue, S. Matsushita, K. Nakanishi, and H. Okano, "Toyota Lean Combustion System - The Third Generation System", SAE Paper No. 930873, 1993
[13] E. H. Mamdani, "Application of fuzzy algorithms for control of simple dynamic plant," Proc. Inst. Elect. Eng. Contr. Sci., vol. 121, pp. 1585- 1588,1974.
[14] ] L. A. Zadeh, "Outline of a new approach to the analysis of complex systems and decision processes," IEEE Trans. Syst., Man, Cybern.,vol.SNC-3,pp. 28-44, 1973.
[15] E. H. Mamdani and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," Int. J. Man-Mach. Stud., vol. 7, pp. 1- 13,1975.
[16] ] M. Braae and D. A. Rutherford, "Selection of parameters for a fuzzy logic controller," Fuzzy Sets Syst., vol. 2, pp. 185-199,1979.
[17] H. Ying, W. Siler, and J. J. Buckley, "Fuzzy control theory: A nonlinear case," Automatica, vol. 26, pp. 513-520, 1990.
[18] H. Ying, "The simplest fuzzy controllers using different inference methods are different nonlinear proportional-integral controllers with variable gains," Automatica, vol. 29, pp. 1579-1589, 1993.
[19] C.-L. Chen and F.-C. Kuo, "Design and analysis of a fuzzy logic controller," Int. J. Syst. Sci., vol. 29, pp. 1579-1589, 1995.
[20]
[21] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst., Man, Cybern. vol. SMC-15, pp. 116-132, 1985.
[22] Z.-Y. Zhao, M. Tomizuka, and S. Isaka, "Fuzzy gain scheduling of PID controllers," IEEE Trans. Syst., Man, Cybern., vol. 23, pp. 1392- 1398,1993.
[23] C. W. de Silva, Intelligent Control: Fuzzy Logic Applications. NewYork: CRC, 1995.
[24] B.-G. Hu, G. K. I. Mann, and R. G. Gosine, "Theoritic and genetic design of a three-rule fuzzy PI controller," in Proc. 6th IEEE Int. Conf. Fuzzy Systems, Barcelona, Spain, July 1-5, 1997, vol. 1, pp. 489-496.
[25] Haluska, P., & Guzzella, L. (1998). Control oriented modeling of mixture formation phenomena in multi-port injection SI gasoline engines. In T. Morel (Ed.), SAE Special Publication (Vol. SP-1330), SAE Paper 980628. Warrendale: Sae International.
[26] Aquino, C. F. (1981). Transient A/F control characteristics of the 5 liter central fuel injection engine. SAE Paper 810494, Sae International, Warrendale.
[27] Hendricks E., Sorenson S.C. "SI engine control and mean value engine modeling"SAE 910258,1991.
[28] Gambino, M., Pianese, C., & Rizzo, G. (1994). Identification of a dynamic model for transient mixture formation in a multipoint spark ignition engine. Proceedings of Fourth ASME Symposium on Transportation Systems (DSC-Vol. 54, pp. 189-204), Chicago.
[29] Arsie , C.Pianese , G. Rizzo , V. Cioffi ., 2002. An adaptive estimator of fuel film dynamics in the intake port of a spark ignition engine.IEEE
[30] Hendricks E. "A generic mean value engine model for spark ignition engines."SIMS 2000.
[31] Hendricks E.,Chevalier A., Jensen A., Sorenson S.C. "Modelling of the intake manifold filling dynamics."SAE paper 960037,1996.
[32] D. L. Stivender, "Engine air control - basis of a vehicular systems control hierarchy," SAE Paper No. 780346, 1978.
[33] R. L. Woods, "An air-modulated fluidic fuel-injection system," ASME J. Dyn. Sys., Meas., and Contr., vol. 101, pp. 71-76, Mar. 1979
[34] N. Gulley, and J.S.R. Jang, Fuzzy logic toolbox: user-s guide, The MATHWORKS, Inc., 2000.
[35] S.W. Wang, D.L. Yu , J.B. Gomm, G.F. Page, S.S. Douglas., "Adaptive neural network model based predictive control for air-fuel ratio of SI engine", . Engineering Applications of Artificial Intelligence 19 (2006) 189-200.