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Feedforward-Compensated PI Controller Design for Air–Fuel Ratio System Control Using Enhanced Weighted Mean of Vectors Algorithm

Feedforward-Compensated PI Controller Design for Air–Fuel Ratio System Control Using Enhanced... The air–fuel ratio (AFR) system helps reducing the rate of harmful pollutants in lean combustion spark-ignition engines and achieving optimal fuel consumption, thus, has a significant role in terms of protecting the environment and the consumer’s budget. The AFR system includes time delays and presents uncertainties due to existing subsystems and requires an effective control method. Therefore, in this study, a feedforward (FF) compensated proportional-integral (PI) control method based on the enhanced weighted mean of vectors algorithm (En-INFO) is proposed for more effective control of the AFR system. The developed En-INFO algorithm was used to optimally determine the coefficients of the PI + FF controller. The initial performance of the En-INFO algorithm was tested against benchmark functions comparatively and its superiority was confirmed. To achieve optimal tuning of PI + FF controller, a modified integral of squared error objective function was also proposed. The AFR system control was performed using the optimal controller coefficients calculated by the En-INFO algorithm. The performance of the developed control structure was comparatively demonstrated using several analyses such as transient response, tracking performance, disturbance rejection and Padé approach techniques. The results revealed that the PI + FF control method based on the proposed En-INFO algorithm can be used as an effective method for the AFR system control. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Arabian Journal for Science and Engineering Springer Journals

Feedforward-Compensated PI Controller Design for Air–Fuel Ratio System Control Using Enhanced Weighted Mean of Vectors Algorithm

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References (34)

Publisher
Springer Journals
Copyright
Copyright © King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2193-567X
eISSN
2191-4281
DOI
10.1007/s13369-023-07724-w
Publisher site
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Abstract

The air–fuel ratio (AFR) system helps reducing the rate of harmful pollutants in lean combustion spark-ignition engines and achieving optimal fuel consumption, thus, has a significant role in terms of protecting the environment and the consumer’s budget. The AFR system includes time delays and presents uncertainties due to existing subsystems and requires an effective control method. Therefore, in this study, a feedforward (FF) compensated proportional-integral (PI) control method based on the enhanced weighted mean of vectors algorithm (En-INFO) is proposed for more effective control of the AFR system. The developed En-INFO algorithm was used to optimally determine the coefficients of the PI + FF controller. The initial performance of the En-INFO algorithm was tested against benchmark functions comparatively and its superiority was confirmed. To achieve optimal tuning of PI + FF controller, a modified integral of squared error objective function was also proposed. The AFR system control was performed using the optimal controller coefficients calculated by the En-INFO algorithm. The performance of the developed control structure was comparatively demonstrated using several analyses such as transient response, tracking performance, disturbance rejection and Padé approach techniques. The results revealed that the PI + FF control method based on the proposed En-INFO algorithm can be used as an effective method for the AFR system control.

Journal

Arabian Journal for Science and Engineering – Springer Journals

Published: Sep 1, 2023

Keywords: Air–fuel ratio system control; Enhanced INFO algorithm; Metaheuristic; Artificial intelligence; Feedforward-compensated PI controller

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