A parameter-varying filtered PID strategy for air–fuel ratio control of spark ignition engines

Ebrahimi, Behrouz; Tafreshi, Reza; Masudi, Houshang; Franchek, Matthew A.; Mohammadpour, Javad; Grigoriadis, Karolos

doi:10.1016/j.conengprac.2012.04.001

Cited by 60 publications

(29 citation statements)

References 17 publications

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“…Consider the internal dynamics of the AFR system with the known upper bound of (11). Applying (10) to internal dynamics of the AFR system with disturbances and time-varying delay (5) gives a finite time in which to reach the sliding surface (7) such that:…”

Section: Stability Proofmentioning

confidence: 99%

“…Further, an adaptive and model-predictive controller for SI engine AFR control was proposed by Kenneth et al [9], who estimated the model and disturbance states of the system by a Kalman filter. Recently, Ebrahimi et al [10,11] proposed a second-order sliding mode and a parameter-varying filtered PID strategy for AFR control of SI engines. These methods can remove the effects of time-varying delay, canister purge disturbance, and measurement noise.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Wu¹,

Tafreshi²

2017

Asian Journal of Control

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Minimization of emissions of carbon dioxide and harmful pollutants and maximization of fuel economy for lean-burn spark ignition (SI) engines relies to a large extent on precise air-fuel ratio (AFR) control. However, the main challenge of AFR control is the large time-varying delay in lean-burn engines. Since the system is usually subject to external disturbances and uncertainties, a high level of robustness in AFR control design must be considered. We propose a fuzzy sliding-mode control (FSMC) to track the desired AFR in the presence of periodic disturbances. The proposed method is model free and does not need any system characteristics. Based on the fuzzy system input-output data, two scaling factors are first employed to normalize the sliding surface and its derivative. According to the concept of the if-then rule, an appropriate rule table for the logic system is designed. Then, based on Lyapunov stability criteria, the output scaling factor is determined such that the closed-loop stability of the internal dynamics with uniformly ultimately bounded (UUB) performance is guaranteed. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated under various operating conditions. The baseline controllers, namely, a PI controller with Smith predictor and sliding-mode controller, are also used to compare with the proposed FSMC. It is shown that the proposed FSMC has superior regulation performance compared to the baseline controllers.

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Section: Stability Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Wu¹,

Tafreshi²

2017

Asian Journal of Control

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“…To challenge the air-fuel ratio control problem, various research works have been reported in the past, such as PID control (Ebrahimi et al, 2012), sliding model control (Pace and Zhu, 2009), feed-forward controller with a purge event fueling system using a transport delay model (Zamanian et al, 2015), neural network feed-forward-feedback control (Zhai and Yu, 2008), linear quadratic (LQ) control (Pace and Zhu, 2014) and model based predictive control (MPC) (Wong et al, 2014). In recent, the generalized predictive controller (GPC) is widely in use due to the advantage covering the area of multi-variable, time-varying and delay in engine system with considering the assumptions of better accuracy in model prediction and optimizer efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cyclic model based generalized predictive control of air-fuel ratio for gasoline engines

Kumar

Shen

2016

JTST

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In four stroke internal combustion engines, optimization of engine performance with air-fuel ratio close to stoichiometric condition is still a challenging task specially in transient operation due to cycle-to-cycle coupling of combustion phenomena and gas dynamics in cylinder. In this paper, the cycle-to-cycle in-cylinder gas dynamics coupling model based air-fuel ratio control using the generalized predictive control law has been discussed and validated in which the input parameters of the discrete time model are updated on cyclic event based. With the discrete time model, a Kalman filter-based state variables such as total fuel mass, unreacted air and residual burnt gas are estimated and used to calculated the in-cylinder air-fuel ratio which reflect the cycle-to-cycle coupling effects of residual gas mass. Then based on model, a controller is designed to achieve the air-fuel control. Apart from this, the control performances of generalized predictive controller and PI controller have been compared. Finally, experimental validation results are demonstrated to show the effectiveness of proposed control scheme that is conducted on a full-scaled gasoline engine test bench.

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“…To address the AFR control problem for SI engines, the most popular approach is proportional-integral (PI) control together with an open-loop feedforward control element, based on lookup tables [6]. However, the compilation of the lookup tables is time-consuming as it requires significant calibration and tuning phases.…”

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confidence: 99%

Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation

Chen

Herrmann

et al. 2017

2017 9th International Conference on Modelling, Identification and Control (ICMIC)

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Abstract-This paper presents a novel adaptive controller of air-fuel ratio (AFR) in spark ignition (SI) engines. The controller robustly estimates unknown time-varying engine parameters and thus improves both the transient and steady-state performance. The objective is to regulate the AFR in the combustion chamber around the stoichiometric value by manipulating the injected fuel mass flow rate so as to improve fuel economy and to reduce emissions. The AFR regulation problem is first reformulated into a tracking control problem of the fuel mass flow. This simplifies the control synthesis, i.e. the number of parameters to be online updated can be reduced. Then a representation of the parameter estimation error is derived by using auxiliary filter operations, which is used as a new leakage term in the adaptive law. In this case, exponential convergence of the AFR error and the estimation of the time-varying parameters can be proved simultaneously. The proposed controller is compared with a generic adaptive controller using the gradient descent method based on a well-calibrated mean value engine model (MVEM). Finally, the proposed controller is also validated with a commercial engine simulation software, GT-Power, demonstrating better results for the suggested adaptive controller than for the gradient descent approach.

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A parameter-varying filtered PID strategy for air–fuel ratio control of spark ignition engines

Cited by 60 publications

References 17 publications

Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Cyclic model based generalized predictive control of air-fuel ratio for gasoline engines

Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation

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