Design of Gain-Scheduled Fuzzy PID Controller for AFR Control System of SI-Based Motorcycle Engine Model

Panjaitan, Joshua Harbangan Dharmarakel; Kurniahadi, Andra; Triwiyatno, Aris; Setiawan, Iwan

doi:10.1109/icitacee50144.2020.9239250

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The simulation results demonstrated the effectiveness and robustness of the proposed control scheme, outperforming the PI controller with Smith predictor under different operating conditions. It is feasible to extend the similar works that is mentioned above [15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 84%

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: Introductionmentioning

confidence: 84%

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

Izci,

Ekinci,

Hussien

2023

PLoS ONE

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“…There are other types of similar control strategies for AFR control, such as fuzzy PID, adaptive control [ 13 ]. While fuzzy PID demonstrates capability for online parameter adjustment, it should be noted that formulating fuzzy rules and sets for complex nonlinear systems necessitates substantial iterative adjustments based on experimental data, making the entire process more intricate and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Hydrogen doping control method for gasoline engine acceleration transient air-fuel ratio

Hu,

Zhang,

et al. 2024

Heliyon

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iTCLab Temperature Monitoring and Control System Based on PID and Internet of Things (IoT)

Rahmat

Waluyo

Rachmanto

et al. 2023

Food Sustainability, Environmental Awareness, and Adaptation and Mitigation Strategies for Developing Countries

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The rapid increase in applications that combine modern concepts and innovations, due to the development of the internet of things (IoT) and cloud computing around the world, make all areas of life continue to move towards an advanced and intelligent society. This innovation continues to enter almost all fields, ranging from simple to complex innovations. In this chapter, IoT is used as a means for tuning PID parameters, when the error does not converge to zero. The experimental results show that the PID parameter tuning process can be done through IoT. And the results are quite encouraging, as an alternative way of tuning PID parameters.

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Design of Gain-Scheduled Fuzzy PID Controller for AFR Control System of SI-Based Motorcycle Engine Model

Cited by 3 publications

References 25 publications

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

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

Hydrogen doping control method for gasoline engine acceleration transient air-fuel ratio

iTCLab Temperature Monitoring and Control System Based on PID and Internet of Things (IoT)

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