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

İzci, Davut; Köse, Ercan; Ekinci, Serdar

doi:10.1007/s13369-023-07724-w

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Due to the time-delayed nature of the AFR system, a feedforward (FF) control mechanism ( K F /(1+ sT F )) with a proportional-integral (PI) controller ( K P + K I / s ) is utilized in this study. The adoption of the FF control mechanism aims to enable efficient system response to changes [ 8 ]. Fig 3 presents the block diagram of the AFR system with the feedforward-compensated PI controller employed in this work.…”

Section: Modeling Of Afr System and Proposed Design Methodologymentioning

confidence: 99%

“…Consequently, managing the air-fuel ratio (AFR) system around this stoichiometric level becomes paramount to maximize conversion efficacy. Therefore, the utilization of robust control mechanisms for the AFR system is essential to enhance the performance of spark-ignition engines and notably reduce harmful emissions [ 8 ]. Nevertheless, controlling the AFR system poses a formidable challenge due to its nonlinear and time-delayed nature [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…To address this complexity, researchers have proposed various control mechanisms. For example, the study reported in [ 8 ] proposed a feedforward (FF) compensated proportional-integral (PI) control method using the enhanced weighted mean of vectors algorithm to effectively control the AFR system in lean combustion spark-ignition engines. The proposed algorithm was used to determine the coefficients of the controller.…”

Section: Introductionmentioning

confidence: 99%

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An elite approach to re-design Aquila optimizer for efficient AFR system control

Izci,

Ekinci,

Hussien

2023

PLoS ONE

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Controlling the air-fuel ratio system (AFR) in lean combustion spark-ignition engines is crucial for mitigating emissions and addressing climate change. In this regard, this study proposes an enhanced version of the Aquila optimizer (ImpAO) with a modified elite opposition-based learning technique to optimize the feedforward (FF) mechanism and proportional-integral (PI) controller parameters for AFR control. Simulation results demonstrate ImpAO’s outstanding performance compared to state-of-the-art algorithms. It achieves a minimum cost function value of 0.6759, exhibiting robustness and stability with an average ± standard deviation range of 0.6823±0.0047. The Wilcoxon signed-rank test confirms highly significant differences (p<0.001) between ImpAO and other algorithms. ImpAO also outperforms competitors in terms of elapsed time, with an average of 43.6072 s per run. Transient response analysis reveals that ImpAO achieves a lower rise time of 1.1845 s, settling time of 3.0188 s, overshoot of 0.1679%, and peak time of 4.0371 s compared to alternative algorithms. The algorithm consistently achieves lower error-based cost function values, indicating more accurate control. ImpAO demonstrates superior capabilities in tracking the desired input signal compared to other algorithms. Comparative assessment with recent metaheuristic algorithms further confirms ImpAO’s superior performance in terms of transient response metrics and error-based cost functions. In summary, the simulation results provide strong evidence of the exceptional performance and effectiveness of the proposed ImpAO algorithm. It establishes ImpAO as a reliable and superior solution for optimizing the FF mechanism-supported PI controller for the AFR system, surpassing state-of-the-art algorithms and recent metaheuristic optimizers.

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Section: Modeling Of Afr System and Proposed Design Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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