Hybrid Fault-Tolerant Control for Air-Fuel Ratio Control System of Internal Combustion Engine Using Fuzzy Logic and Super-Twisting Sliding Mode Control Techniques

Alsuwian, Turki; Riaz, Umar; Amin, Arslan Ahmed; Qadir, Muhammad Bilal; Almasabi, Saleh; Jalalah, Mohammed

doi:10.3390/en15197010

Cited by 9 publications

(6 citation statements)

References 43 publications

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“…Fig 2 illustrates the configuration of an air-fuel ratio (AFR) system, which includes the three-way catalytic (TWC) converter, throttle, fuel path, heat exhaust gas oxygen (HEGO) sensor, lean nitrogen oxide trap (LNT), and universal exhaust gas oxygen (UEGO) sensor [ 53 ]. The control of this system is challenging due to cycle and gas transportation delays.…”

Section: Modeling Of Afr System and Proposed Design Methodologymentioning

confidence: 99%

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%

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

Izci,

Ekinci,

Hussien

2023

PLoS ONE

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“…According to the authors in References 21 and 22, the total mass fuel flow ( ṁf ) injected into the cylinders is calculated by Equation (8). Where ( ṁfl ) and ( ṁfv ) are the fuel film flow (9) and the fuel vapor fraction (10) into the cylinder, respectively.…”

Section: Intake Manifold Pressure and Temperature Modelmentioning

confidence: 99%

“…In the literature can be found research works based on vibration signals or sound signals analyses 4 for fault detection. In References 4‐7, the authors focused their research on artificial neural networks (ANN) and fuzzy logic 8,9 to design FTC and FD systems. Authors in Reference 4 proposed a fault diagnosis system sound‐based, which is based on the emitted sound of the ICE when running at idle conditions using the extension neural network (ENN) over using the traditional multilayer (ANN).…”

Section: Introductionmentioning

confidence: 99%

“…In Reference 7, a semi‐physical adaptation of a neural network to develop an FD system for an ICE is used. The authors in Reference 8 proposed a hybrid FTC for the AFR control system of an ICE using fuzzy logic and super‐twisting sliding mode control techniques. The FDI system was designed using fuzzy logic as a part of the active fault tolerant control.…”

Section: Introductionmentioning

confidence: 99%

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Sensor fault‐tolerant control for an internal combustion engine

Escobar‐Jiménez,

Ocampo‐Rodríguez,

Osorio‐Gordillo

et al. 2023

Adaptive Control & Signal

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SummaryThis research presents the design of a fault‐tolerant system based on a dual extended Kalman filter (DEKF) and a bank of two high‐gain observers to ensure the continuous operation of an internal combustion engine (ICE) despite total and partial faults in the mass airflow (MAF) sensor, temperature sensor, and pressure sensor. The DEKF aims to estimate the air mass flow rate () through the throttle valve. The DEKF uses the temperature and pressure measured in the intake manifold to accurately estimate the (), a failure in any of both sensors will not allow the correct functioning of the DEKF. Therefore, a bank of two high‐gain observers is proposed to estimate the pressure and temperature from only one measure. Observer 1 estimates the temperature and pressure from the pressure, and Observer 2 uses the temperature for estimating pressure and temperature. Hence, if the temperature or pressure sensors fail, the estimated temperature or pressure will replace the ill‐measure signal. The results show the effectiveness of the proposed fault‐tolerant system in the case of one sensor or multiple sensor faults.

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“…Before producing the output, the inputs given to the FLC must go through the three fundamental processes of fuzzification, decision-making, and defuzzification. With the aid of specified membership functions (MFs), the input variable is turned into a linguistic variable during the fuzzification stage [29].…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network

2022

Self Cite

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Due to instability issues in autonomous vehicles, the risk of danger is increasing rapidly. These problems arise due to unwanted faults in the sensor or the actuator, which decrease vehicle efficiency. In this modern era of autonomous vehicles, the risk factor is also increased as the vehicles have become automatic, so there is a need for a fault-tolerant control system (FTCS) to avoid accidents and reduce the risk factors. This paper presents an active fault-tolerant control (AFTC) for autonomous vehicles with a fuzzy neural network that can autonomously identify any wheel speed problem to avoid instability issues in an autonomous vehicle. MATLAB/Simulink environment was used for simulation experiments and the results demonstrate the stable operation of the wheel speed sensors to avoid accidents in the event of faults in the sensor or actuator if the vehicle becomes unstable. The simulation results establish that the AFTC-based autonomous vehicle using a fuzzy neural network is a highly reliable solution to keep cars stable and avoid accidents. Active FTC and vehicle stability make the system more efficient and reliable, decreasing the chance of instability to a minimal point.

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Hybrid Fault-Tolerant Control for Air-Fuel Ratio Control System of Internal Combustion Engine Using Fuzzy Logic and Super-Twisting Sliding Mode Control Techniques

Cited by 9 publications

References 43 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

Sensor fault‐tolerant control for an internal combustion engine

An Autonomous Vehicle Stability Control Using Active Fault-Tolerant Control Based on a Fuzzy Neural Network

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