Stochastic adaptive air-fuel ratio control of spark ignition engines

Yang, Jun; Shen, Tielong; Jiao, Xiaohong

doi:10.1002/tee.21991

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…Step 2. Calculate the difference in the sliding surface (equation (6)). Ds½k is the second input to the proposed DFSMC.…”

Section: Stability Proofmentioning

confidence: 99%

“…A cyclic transient characteristic of the air mass and the fuel mass with consideration of residual gas variation was represented in discrete-time and was then controlled by a proposed stochastic adaptive controller. 6 A linear quadratic (LQ) tracking controller was designed by Pace and Zhu 7 to optimally track the desired AFR by minimizing the error between the trapped in-cylinder air mass and the product of the desired AFR and fuel mass. Recently, a generalized predictive control (GPC) was proposed by Kumar and Shen 8 to achieve AFR control based on the cycle-tocycle in-cylinder gas dynamic coupling model.…”

Section: Introductionmentioning

confidence: 99%

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Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Tafreshi

2019

Advances in Mechanical Engineering

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Air-fuel ratio is a key factor for the minimization of the harmful pollutant emissions and maximization of fuel economy. However, a big challenge for air-fuel ratio control is a large time-varying delay existing in spark ignition engines. In this article, a digital fuzzy sliding-mode controller is proposed to control a linear parameter-varying sampled-data air-fuel ratio system. First, the Pade first-order technique is utilized to approximate the time-varying delay. The resultant system-a linear parameter-varying continuous-time air-fuel ratio system with unstable internal dynamics-is then discretized to a linear parameter-varying sampled-data air-fuel ratio system appropriate for a discrete-time control approach. Based on the linear parameter-varying sampled-data air-fuel ratio system, a stable sliding surface with a desired tracking error dynamics is presented. Two input scaling factors and one output scaling factor are determined for the proposed digital fuzzy sliding-mode controller. Then, the fuzzy inference is executed through a look-up table to stabilize the sliding surface into a convex set, and then make the tracking error possess uniformly ultimately bounded performance. The overall system stability is verified by Lyapunov's stability criteria. Finally, the simulation results demonstrate the feasibility, effectiveness, and robustness of the proposed control scheme under different operating conditions and show the superiority of the proposed approach performance compared to the baseline controller.

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“…Step 2. Calculate the difference in the sliding surface (equation (6)). Ds½k is the second input to the proposed DFSMC.…”

Section: Stability Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Tafreshi

2019

Advances in Mechanical Engineering

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“…al. [25] proposed a discrete-time dynamic model to represent the cyclic transient characteristics of the air-fuel mass with consideration of residual gas variation. In absence of lambda sensor, Kumar and Shen [26] present a model-based estimation and feedback control for cyclic air-to-fuel ratio of spark-ignition engines.…”

Section: Introductionmentioning

confidence: 99%

Design and Simulation of Air-Fuel Percentage Sensors in Drone Engine Controlling

et al. 2022

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This paper presents the design and simulation of air-fuel percentage sensors in drone engine control using Matlab. The applications of sensor engineering system have been pioneer in technology development and advancement of automated machine as complex systems. The integration of drone fuel sensor system is the major series components such as injector, pumps and switches. The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring. The sensor system is improved and virtualized in Simulink block set by varying the parameters with high range to observe the fuel utilization curves and extract the validated results. The obtained results show that the possibility of engine operation in critical conditions such as takeoff, landing, sharp maneuver and performance is applicable to turn off the system in case of break down in the sensor to ensure the safety of drone engine. HIGHLIGHTS The drone engine fuel rate sensor is designed and examined to determine the air-to-fuel ratio The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring The obtained results show that the possibility of using engine with different failure mode and fault considerations The represented control structure is simple, efficient and provides the required air-to-fuel ratio

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“…But the predictor is available only when the parameters in the model match those in the plant, and the performance may decrease when modeling errors exist. Moreover, other challenges in maintaining the AFR at the stoichiometric value involve the complications in the model construction of the engine and some physicochemical processes such as the air intake [4], fuel delivery, and the combustion. Thus, many algorithms have been proposed to get an appropriate solution, such as the neural network control algorithm applied in [5][6][7], which can identify the engine model for controlling the AFR.…”

Section: Introductionmentioning

confidence: 99%

ADRC‐based transient air/fuel ratio control with time‐varying transport delay consideration for gasoline engines

Wang

Jiao

2017

IEEJ Transactions Elec Engng

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This paper presents a fuel injection controller based on the modified active disturbance rejection control (ADRC) algorithm to maintain the air/fuel ratio (AFR) at the stoichiometric value in the presence of a transport time delay. The factors affecting the dynamics of the AFR include the variations of the intake manifold pressure, engine speed, and load torque, as well as uncertain parameters existing in the fuel film evaporation and the oxygen sensor aging, which are viewed as the 'total disturbance' to the AFR system and estimated by the extended state observer (ESO). Thus, based on the Lyapunov–Krasovskii functional stability theory, the ADRC fuel injection controller is designed with the gain matrices of the controller and observer and solved by employing linear matrix inequalities. Considering the time‐varying transport delay, a time delay block is added to the ESO. By synchronizing the input signals of the ESO, the performance in terms of the timely compensation for the disturbance is improved. The effectiveness of the proposed control strategy is validated through simulation of a V6 spark ignition engine model developed by the Society of Instrument and Control Engineers (SICE) Research Committee on Advanced Powertrain Control Theory. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Stochastic adaptive air-fuel ratio control of spark ignition engines

Cited by 6 publications

References 12 publications

Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Digital fuzzy sliding-mode control for a linear parameter-varying air–fuel ratio system

Design and Simulation of Air-Fuel Percentage Sensors in Drone Engine Controlling

ADRC‐based transient air/fuel ratio control with time‐varying transport delay consideration for gasoline engines

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