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An elite approach to re-design Aquila optimizer for efficient AFR system control
Davut Izci1,2, Serdar Ekinci1, Abdelazim G Hussien3,4
1Department of Computer Engineering, Batman University, Batman, Turkey.
Plos One
|September 20, 2023
Summary
This study introduces an enhanced Aquila optimizer (ImpAO) for precise air-fuel ratio (AFR) control in lean combustion engines. ImpAO significantly improves control accuracy and transient response, offering a superior solution for emissions reduction.
Area of Science:
- Automotive Engineering
- Control Systems
- Combustion Science
Background:
- Optimizing air-fuel ratio (AFR) control in lean combustion spark-ignition engines is vital for reducing emissions and combating climate change.
- Existing control methods require enhancement to meet stringent environmental regulations and improve engine efficiency.
Purpose of the Study:
- To develop and evaluate an enhanced Aquila optimizer (ImpAO) for optimizing feedforward (FF) and proportional-integral (PI) controller parameters in AFR control systems.
- To demonstrate the superiority of ImpAO over state-of-the-art and metaheuristic algorithms in terms of control accuracy, stability, and transient response.
Main Methods:
- An enhanced Aquila optimizer (ImpAO) incorporating a modified elite opposition-based learning technique was developed.
- ImpAO was utilized to optimize the parameters of a feedforward (FF) mechanism and a proportional-integral (PI) controller for AFR control.
- Simulation studies were conducted to compare ImpAO's performance against established and recent metaheuristic algorithms.
Main Results:
- ImpAO achieved a minimum cost function value of 0.6759, demonstrating robust and stable performance (average ± std dev: 0.6823±0.0047).
- Statistical analysis (Wilcoxon signed-rank test) confirmed significant performance differences (p<0.001) compared to other algorithms.
- ImpAO exhibited superior transient response metrics, including lower rise time (1.1845 s) and settling time (3.0188 s), with reduced overshoot (0.1679%).
Conclusions:
- The proposed ImpAO algorithm offers a highly effective and reliable solution for optimizing AFR control systems.
- ImpAO surpasses existing state-of-the-art and metaheuristic algorithms in control accuracy, transient performance, and computational efficiency.
- ImpAO represents a significant advancement in AFR control for lean combustion engines, contributing to emissions mitigation and climate change efforts.
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