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Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)
Subtitle of host publicationProceedings of a meeting held 10-12 July 2017, Kunming, China
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages1074-1079
Number of pages6
ISBN (Electronic)9781509065752
ISBN (Print)9781509065769
DOIs
DateAccepted/In press - 12 May 2017
DateE-pub ahead of print - 22 Mar 2018
DatePublished (current) - May 2018

Abstract

This paper presents a novel adaptive controller of air-fuel ratio (AFR) in spark ignition (SI) engines. The controller robustly estimates unknown time-varying engine parameters and thus improves both the transient and steady-state performance. The objective is to regulate the AFR in the combustion chamber around the stoichiometric value by manipulating the injected fuel mass flow rate so as to improve fuel economy and to reduce emissions. The AFR regulation problem is first reformulated into a tracking control problem of the fuel mass flow. This simplifies the control synthesis, i.e. the number of parameters to be online updated can be reduced. Then a representation of the parameter estimation error is derived by using auxiliary filter operations, and used as a new leakage term in the adaptive law. In this case, exponential convergence of the AFR error and the estimation of the time-varying parameters can be proved simultaneously. The proposed controller is compared with a generic adaptive controller using the gradient descent method based on a well-calibrated mean value engine model (MVEM). Finally, the proposed controller is also validated with a commercial engine simulation software, GT-Power, demonstrating better results than for the gradient descent approach.

    Research areas

  • Air-fuel ratio control, spark ignition engines, adaptive control, parameter estimation, GT-Power simulation

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