Adaptive and Learning Control for SI Engine Model With Uncertainties

Tang, Huajin; Weng, Larry; Zhang, Dong; Yan, Rui

doi:10.1109/tmech.2008.2004806

Cited by 43 publications

(17 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the SI engine injection control system, the EGO sensor output values read by an electronic control unit (ECU) do not directly describe the condition of the AFR in the cylinder (combustion chamber) at the same time, because of the influence of transport delay which is around 250 ms [4]. This transport delay is affected by the dynamics of the process of mixing air and fuel in the combustion chamber, the length of duct, the exhaust, and the dynamics of the EGO sensor itself as presented in Fig.…”

Section: Iatmentioning

confidence: 99%

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

Badmianto

Firmansyah

Cahyadi

2018

IJITEE

View full text Add to dashboard Cite

Abstract-In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%. (model) is required to obtain an accurate mathematical model. A good model is a model that more accurately reflects the dynamics of the system or plant. The dynamics of air-fuel ratio (AFR) on SI injection system Engine is very non-linear and multivariable [1], makes the process of the parameter identification difficult compared with the linear system and a few variables. Keywords--ParameterThe AFR is an important indicator that affects exhaust emissions, engine power, and fuel consumption in Spark Ignition (SI) Engine. The influence of lambda (normalized AFR) on power and fuel consumption are presented in Fig. 1. Maximum power will be achieved if the lambda value is in the range of 0.82 (AFR = 12:1) [2], while the fuel consumption will be achieved if the minimum number lambda is in the range of 1:09 (AFR = 16:1) [2].

show abstract

Section: Iatmentioning

confidence: 99%

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

Badmianto

Firmansyah

Cahyadi

2018

IJITEE

View full text Add to dashboard Cite

show abstract

“…As for the parameter uncertainties in the AFR control plant, the adaptive control algorithm is widely employed, typical examples being given in [10][11][12][13]. On the basis of the mean value engine model, adaptive fuel injection controllers are derived in [10,11] to remove the influence of the uncertain parts caused by the fuel film evaporation and the system description. Adaptive update laws are derived on the Lyapunov design for the uncertain parameters [12,13].…”

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

View full text Add to dashboard Cite

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.

show abstract

“…In [12] air-fuel ratio control for watercrafts is developed based on feedback linearization approach, while load estimation is performed with the use of Kalman Filtering. In [13] a global linearizing feedback control feedback control method is provided for combustion engine models. To cope with model uncertainties the control algorithm is supplied with adaptive features.…”

Section: Introductionmentioning

confidence: 99%

Flatness-based embedded control of air-fuel ratio in combustion engines

Rigatos¹,

Siano²,

Arsie³

2014

AIP Conference Proceedings

View full text Add to dashboard Cite

Articles you may be interested inFlatness-based embedded adaptive fuzzy control of spark ignited engines AIP Conf. Proc. 1618, 241 (2014); 10.1063/1.4897720 Flatness-based nonlinear embedded control and filtering for spark-ignited engines AIP Conf. Proc. 1618, 231 (2014); 10.1063/1.4897719Flatness-based embedded adaptive fuzzy control of turbocharged diesel engines AIP Conf.Abstract. A nonlinear controller is designed for air-fuel ratio control in combustion engines, making use of differential flatness theory and of the Derivative-free nonlinear Kalman Filter. It is proven that the air-fuel ratio system is a differentially flat one and admits dynamic feedback linearization. Using a change of variables that is based on differential flatness theory it is shown that the air-fuel ratio system can be transformed to the linear canonical form, for which the design of a state feedback controller is easier. Moreover, to compensate for modeling uncertainties and external disturbances the Derivativefree nonlinear Kalman Filter is designed as a disturbance observer. The estimation of the perturbations that effect the air-fuel systems enables their compensation through the inclusion of an additional term in the feedback control law. The efficiency of the proposed nonlinear feedback control scheme is tested through simulation experiments.

show abstract

Adaptive and Learning Control for SI Engine Model With Uncertainties

Cited by 43 publications

References 15 publications

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

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

Flatness-based embedded control of air-fuel ratio in combustion engines

Contact Info

Product

Resources

About