Control of Camless Intake Process—Part II1

Ashhab, Moh’d Sami; Stefanopoulou, Anna G.; Cook, J.A.; Levin, Michael

doi:10.1115/1.482448

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…It consists of a feedforward controller, a CAC estimator and an on-line parameter estimator which is used to adapt the feedforward controller parameters using the CAC estimate. It has been shown in [1] that the individual cylinder air charge measurements can be extracted from two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF) and the intake manifold absolute pressure (MAP) sensors. The estimated cylinder air charge is used therein to update the feedforward controller parameters via the on-line parameter estimator in order to account for modeling errors and engine parts degradation.…”

Section: Feedback Adaptive Controllermentioning

confidence: 99%

“…A feedback control system that is responsible for this task has been developed in [1]. It consists of a feedforward controller, a CAC estimator and an on-line parameter estimator which is used to adapt the feedforward controller parameters using the CAC estimate.…”

Section: Introductionmentioning

confidence: 99%

“…The ANN model forecasts the CAC in the future at this speed. It has been shown in [2] that the feedforward controller developed in [1] can be modeled by inverting the camless engine ANN model. The ANN based feedforward controller is more concise and easier to adapt.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the basic control system of such an engine was designed in [1]. Improvements of the initial control system design that enhanced the performance of the system through computer simulation were attempted later.…”

Section: Introductionmentioning

confidence: 99%

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Control of 12-Cylinder Camless Engine with Neural Networks

Ashhab

2017

MATEC Web Conf.

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Abstract. The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN's). The inputs to the net are the intake valve lift (IVL) and intake valve closing timing (IVC) whereas the output of the net is the cylinder air charge (CAC). The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver's torque demand) while minimizing the pumping losses (increasing engine efficiency). All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF) and the intake manifold absolute pressure (MAP) sensors. The feedback controller's capability is demonstrated through computer simulation.

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Section: Feedback Adaptive Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Control of 12-Cylinder Camless Engine with Neural Networks

Ashhab

2017

MATEC Web Conf.

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“…One of the major challenges in the camless IC engine design is controlling the cylinder air charge (CAC) rapidly and accurately based on conventional measurements. A feedback control system that is responsible for this task has been developed by [1]. It consists of a feedforward controller, a CAC estimator and an on-line parameter estimator which is used to adapt the feedforward controller parameters using the CAC estimate.…”

Section: Introductionmentioning

confidence: 99%

Modeling of 12 Cylinder Camless Engine with Neural Networks

Sami¹

2016

Fifth International Conference on Advances in Mechanical, Aeronautical and Production Techniques - MAPT 2016

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The 12-cyliner camless engine breathing process will be modeled with artificial neural networks (ANN's). The inputs to the net are the intake valve lift (IVL) and intake valve closing timing (IVC) whereas the output of the net is the cylinder air charge (CAC). In camless engine a control system should be designed to track desired cylinder air charge as demanded by the driver and thus satisfy torque requirement. For efficient engine performance the pumping loss (PL) must be minimized while tracking the cylinder air charge. Towards this end, the pumping loss as a function of the intake valve lift and intake valve closing timing is modeled with the aid of the neural networks. The developed neural net model predicts the cylinder air charge and pumping loss well and can be used for camless engine control design.

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Introduction

Mechanical Engineering Series

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Control of Camless Intake Process—Part II1

Cited by 10 publications

References 4 publications

Control of 12-Cylinder Camless Engine with Neural Networks

Control of 12-Cylinder Camless Engine with Neural Networks

Modeling of 12 Cylinder Camless Engine with Neural Networks

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