2019
DOI: 10.3390/en12010173
|View full text |Cite
|
Sign up to set email alerts
|

Adaptive Air-Fuel Ratio Regulation for Port-Injected Spark-Ignited Engines Based on a Generalized Predictive Control Method

Abstract: The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynam… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
6
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
7
1

Relationship

1
7

Authors

Journals

citations
Cited by 15 publications
(6 citation statements)
references
References 24 publications
0
6
0
Order By: Relevance
“…Aiming at efficient design and tuning distillation controllers, studies based on advanced techniques have been the focus of several works in the last decades. Karacan, Hapoglu, and Alpbaz [10] and Meng et al [11] used generalized predictive control; Karacan [12] applied nonlinear long-range predictive control; Rani, Singh, and Gupta [13] and Ahmed and Khalaf [14] implemented artificial intelligence through neural networks; and Miccio and Cosenza [15] did studies with fuzzy logic algorithms. All these studies were successful in reducing transient time; however, its reduction to zero is impossible, since the action of the reboiler must be propagated up to the last stage of the column, and the same occurs inversely with the action of reflux flow rate until the bottom of the column.…”
Section: Introductionmentioning
confidence: 99%
“…Aiming at efficient design and tuning distillation controllers, studies based on advanced techniques have been the focus of several works in the last decades. Karacan, Hapoglu, and Alpbaz [10] and Meng et al [11] used generalized predictive control; Karacan [12] applied nonlinear long-range predictive control; Rani, Singh, and Gupta [13] and Ahmed and Khalaf [14] implemented artificial intelligence through neural networks; and Miccio and Cosenza [15] did studies with fuzzy logic algorithms. All these studies were successful in reducing transient time; however, its reduction to zero is impossible, since the action of the reboiler must be propagated up to the last stage of the column, and the same occurs inversely with the action of reflux flow rate until the bottom of the column.…”
Section: Introductionmentioning
confidence: 99%
“…It is a challenging work to handle the control problems of parameter uncertainties and variations, the time-delay and nonlinearities, the large modeling uncertainties and unknown dynamics, and the wide operating range and complex working conditions. The adaptive AFR controller was introduced to overcome the control problems that are mentioned above [17]. From the definition in Equation (4), the in-cylinder air mass estimation is a crucial part for the fuel injection calculation and it affects the AFR control results.…”
Section: Si Engine Afr Control Problem Formulationmentioning
confidence: 99%
“…The engine ECU was implemented on a Freescale MC9S12XDP512 based controller. An ATI Vision based calibration system was established to acquire the control parameter online updating and internal data logging [17]. effectiveness of the illustrated intake air mass observer and the prediction results of cylinder intake flow.…”
Section: Experimental Test Benchmentioning
confidence: 99%
“…The work in [5] proposes an adaptive stoichiometric AFR control. A generalized predictive control is proposed for the AFR regulation by taking time delays, nonlinearities, and parameter variations into account in the closed-loop in [6]. The work [7] addresses an experimental AFR control with the unknown system dynamics estimator.…”
Section: Introductionmentioning
confidence: 99%