Real-time implementation of multiple model based predictive control strategy to air/fuel ratio of a gasoline engine

• Autorzy: Wojnar S , Polóni T , Šimončič P , Rohal’-Ilkiv B , Honek M , Csambál J
• Języki publikacji: EN

Abstrakty

EN

Growing safety, pollution and comfort requirements influence automotive industry ever more. The use of three-way catalysts in exhaust aftertreatment systems of combustion engines is essential in reducing engine emissions to levels demanded by environmental legislation. However, the key to the optimal catalytic conversion level is to keep the engine air/fuel ratio (AFR) at a desired level. Thus, for this purposes more and more sophisticated AFR control algorithms are intensively investigated and tested in the literature. The goal of this paper is to present for a case of a gasoline engine the model predictive AFR controller based on the multiple-model approach to the engine modeling. The idea is to identify the engine in particular working points and then to create a global engine's model using Sugeno fuzzy logic. Opposite to traditional control approaches which lose their quality beside steady state, it enables to work with satisfactory quality mainly in transient regimes. Presented results of the multiple-model predictive air/fuel ratio control are acquired from the first experimental real-time implementation on the VW Polo 1390 cm3 gasoline engine, at which the original electronic control unit (ECU) has been fully replaced by a dSpace prototyping system which execute the predictive controller. Required control performance has been proven and is presented in the paper.

Słowa kluczowe

EN

model predictive control; multiple models; air/fuel ratio; spark ignition engine; ARX models

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2013
• Tom: Vol. 23, no. 1
• Strony: 93--106
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wzory

Twórcy

autor

Wojnar S

• Department of Automation, Measurement and Applied Informatics, Mechanical Engineering Faculty, Slovak University of Technology, Bratislava, Slovak Republic

autor

Polóni T

• Department of Automation, Measurement and Applied Informatics, Mechanical Engineering Faculty, Slovak University of Technology, Bratislava, Slovak Republic

autor

Šimončič P

• Department of Automation, Measurement and Applied Informatics, Mechanical Engineering Faculty, Slovak University of Technology, Bratislava, Slovak Republic

autor

Rohal’-Ilkiv B

• Department of Automation, Measurement and Applied Informatics, Mechanical Engineering Faculty, Slovak University of Technology, Bratislava, Slovak Republic

autor

Honek M

• Department of Automation, Measurement and Applied Informatics, Mechanical Engineering Faculty, Slovak University of Technology, Bratislava, Slovak Republic

autor

Csambál J

• Department of Automation, Measurement and Applied Informatics, Mechanical Engineering Faculty, Slovak University of Technology, Bratislava, Slovak Republic

Bibliografia

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• [2] J. Bengtsson, P. Strandh, R. Johansson, P. Tunestal, and B. Johansson: Hybrid modeling of homogenous charge compression ignition (HCCI) engine dynamics - a survey. Int. J. of Control, 80(11), (2007), 1814-1847.
• [3] dSPACE GmbH. HelpDesk Application. (2009).
• [4] Z. Hou: Air fuel ratio control for gasoline engine using neural network multi-step predictive model. 3rd Int. Conf. on Intelligent Computing, Qingdao, China, (2007).
• [5] G. Lorini, A. Miotti and R. Scattolini: Modeling, simulation and predictive control of a spark ignition engine. In Predimot (Ed.), Predictive control ofcombustion engines, 39-55. TRAUNER Druck GmbH & CoKG, (2006).
• [6] J. M. Maciejowski: Predictive control with constraints. University of Cambridge, 2000.
• [7] X. Mao, D. Wang, W. Xiao, Z. Liu, J. Wang and H. Tang: Lean limit and emissions improvement for a spark-ignited natural gas engine using a generalized predictive control (GPC)-based air/fuel ratio controller. Energy & Fuels, 23 (2009), 6026-6032.
• [8] D. Q. Mayne, J. B. Rawlings, C. V. Rao and P. O. M. Scokaert: Constrained model predictive control: Stability and optimality. Automatica, 36 (2000), 789-814.
• [9] R. Murray-Smith and T. A. Johanssen: Multiple model approaches to modellingand control. Taylor & Francis, 1997.
• [10] K. R. Muske and J. C. P. Jones: A model-based SI engine air fuel ratio controller. American Control Conf., Minneapolis, USA, (2006).
• [11] T. Polónii, T. A. Johansen and B. Rohal’-Ilkiv: Identification and modeling of air-fuel ratio dynamics of a gasoline combustion engine with weighted arx model network. Trans. of the ASME (Journal of Dynamic Systems, Measurement, andControl), 130(6), (2008). 061009.
• [12] T. Polóni, B. Rohal’-Ilkiv And T.A. Johansen: Multiple ARX model-based air-fuel ratio predictive control for SI engines. In IFAC Workshop on advancedfuzzy and neural control. Valenciennes, France, Conference paper MO5-3, (2007).
• [13] J. Zeman and B. Rohal’-Ilkiv: Robust min-max model predictive control of linear systems with constraints. IEEE Int. Conf. on Industrial Technology, (2003), 930-935.
• [14] Y. J. Zhai, D-W. Yu, H-Y. Guo and D. L. Yu: Robust air/fuel ratio control with adaptive DRNN model and AD tuning. Engineering Applications of ArtificialIntelligence, 23 (2010), 283-289.
• [15] Y. J. Zhai and D. L. Yu: Neural network model-based automotive engine air/fuel ratio control and robustness evaluation. Engineering Applications of Artificial Intelligence, 22 (2009), 171-180.

Uwagi

EN

The work has been supported by the Slovak Research and Development Agency under grants LPP- 0075-09, APVV-0280-06 and LPP-0096-07. This support is very gratefully acknowledged