Air fuel ratio detector corrector for combustion engines using adaptive neuro-fuzzy networks
DOI:
https://doi.org/10.11121/ijocta.01.2013.00152Keywords:
Air-fuel ratio, adaptive learning systems, combustion engines, neuro-fuzzy network, detector, correctorAbstract
A perfect mix of the air and fuel in internal combustion engines is desirable for proper combustion of fuel with air. The vehicles running on road emit harmful gases due to improper combustion. This problem is severe in heavy vehicles like locomotive engines. To overcome this problem, generally an operator opens or closes the valve of fuel injection pump of locomotive engines to control amount of air going inside the combustion chamber, which requires constant monitoring. A model is proposed in this paper to alleviate combustion process. The method involves recording the time-varying flow of fuel components in combustion chamber. A Fuzzy Neural Network is trained for around 40 fuels to ascertain the required amount of air to form a standard mix to produce non-harmful gases and about 12 fuels are used for testing the network’s performance. The network then adaptively determines the additional/subtractive amount of air required for proper combustion. Mean square error calculation ensures the effectiveness of the network’s performance.
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References
Astrom, K J, Wittenmark, B., Adaptive Control, Addison-Wesley (1995).
Cho, D., Hedrick, J.K., A nonlinear controller design method for fuel-injected automotive engines, ASME, Journal of Engineering Gas Turbine Power, 110 (3), 313-320 (1988). CrossRef
Cho, D., Oh, H., Variable structure control method for fuel-injected systems, Journal of Dynamic Systems, Measurement and Control, 115, 475-481 (1993). CrossRef
Choi, S.B., Hedrick, J.K., An observer based controller design method for improving air/fuel characteristics of spark ignition engines, IEEE Transactions on Control System Technology, 6 (3), 325-334 (1988). CrossRef
Kaidantzis, P., Rasmussen, P., Jensen, M., Vesterholm, T., Hendricks, E., Robust, Self-Calibrating Lambda Feedback for SI Engines, SAE 930860 (1993).
Won, M., Choi, S.B., Hedrick, J.K., Air-to-fuel ratio control of spark ignition engines using Gaussian network sliding control, IEEE Transactions on Control System Technology, 6 (5), 678-687 (1998). CrossRef
Yoon, P., Sunwoo, M., An adaptive sliding mode controller for air-fuel ratio control of spark ignition engines, Journal of Automobile Engineering, 215 (D2), 305-315 (2001). CrossRef
Raghuram, P., Ramkumar, P., Sreenivasan, M., Puhan, S., Air-Fuel Ratio Calculations In An Internal Combustion Engine Based On The Cylinder Pressure Measurements, International Journal of Engineering Research and Applications, 2 (6), 1378-1385 (2012).
Yinhua, L., Tielong, S., Kota, S., Kenji, S., Modeling of Individual Cylinder Air-Fuel Ratio for IC Engines with Multi-Cylinders, Proceedings of 30th Chinese Control Conference, 6151-6156 (2012).
Turin, R., Geering, H., Model-Reference Adaptive A/F Ratio Control in an SI Engine Based on Kalman-Filtering Techniques, American Control Conference, 4082-4090 (1995).
Powell, J.D., Fekete, N.P., Chang, C.F., Observer-Based Air-Fuel Ratio Control, IEEE Control Systems Magazine, 18 (5), 72-83 (1998). CrossRef
Mianzo, L., Peng, H., Haskara I., Transient Air-Fuel ratio H∞ Preview Control of a Drive-by-Wire Internal Combustion Engine, American Control Conference, 2867-2871 (2001).
Muske, K.R., Jones, C.P.J., A Model-based SI Engine Air Fuel Ratio Controller, American Control Conference, 3284-3289 (2006).
Chang, C.F., Fekete, N.P., Powell, J.D., Engine Air-Fuel Ratio Control Using an Event-Based Observer, SAE Paper No. 930766 (1993).
Stefanopoulou, A.G., Grizzle, J.W., Freudenberg, J.S., Engine Air-Fuel Ratio and Torque Control using Secondary Throttles, Conference on Decision and Control, 2748 – 2753 (1994).
Zhang, F., Grigoriadis, K., Franchek, M. and Makki, I., Linear Parameter Varying Lean Burn Air-Fuel Ratio Control for a Spark Ignition Engine, Journal of Dynamic Systems, Measurement and Control, 129, 404-414 (2007). CrossRef
Howlett, R.J., Howson, P.A., Walters, S.D., Pashley, N., Determination of air fuel ratio in an automotive ignition system using neural networks, International Symposium on Automotive Technology and Applications, Italy (1996).
Howlett, R.J., Howson, P.A., Walters, S.D., Condition monitoring in an automotive spark ignition engine using a multi-computer neural network, COMADEM, UK (1996).
Howlett, R.J., Condition monitoring and fault diagnosis in car engines, Condition Monitor, Elsevier Science Publications (1996).
Howlett, R.J., Monitoring and control of an internal combustion engine air-fuel ratio using neural and fuzzy techniques, International Symposium on the Engineering of Intelligent Systems, Spain (1998).
Majors, M., Stori, J., Cho, D., Neural network control of automotive fuel-injection systems, IEEE Control Systems Magazine, 14 (3) (1994).
Frith, A.M., Gent, C.R., Beaumont, A.J., Adaptive control of gasoline engine air-fuel ratio using artificial neural networks, IEEE Conference on Artificial Neural Networks, UK (1995). CrossRef
Hanzevack, E.L., Long, T.W., Atkinson, C.M., Traver, M.L., Virtual sensors for spark ignition engines using neural networks, Proceedings of the American Control Conference, U.S. (1997).
Arora, N., Regulating air-fuel balance in combustion engines using CMAC Neural Networks, IEEE Xplore via International Conference on Methods and Models in Computer Science, JNU, New Delhi, India (2009).
John B.L Heywood, Internal Combustion Engine Fundamentals, McGraw-Hill, Inc. (1988).
Shing, J., Jang, R., ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, 23, 665-683 (1993). CrossRef
Tsoukalas, L.H., Uhrig, R.E., Fuzzy and Neural Approaches in Engineering, John Wiley & Sons INC (1997).
Roy, S.S., Design of adaptive neuro-fuzzy inference system for predicting surface roughness in turning operation, Journal of Scientific & Industrial Research, 64, 653-659 (2005).
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