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GAUSSIAN PROCESS REGRESSION FEEDFORWARD CONTROLLER FOR DIESEL ENGINE AIRPATH
Volkan Aran,Mustafa Unel 한국자동차공학회 2018 International journal of automotive technology Vol.19 No.4
Gaussian Process Regression (GPR) provides emerging modeling opportunities for diesel engine control. Recent serial production hardwares increase online calculation capabilities of the engine control units. This paper presents a GPR modeling for feedforward part of the diesel engine airpath controller. A variable geotmetry turbine (VGT) and an exhaust gas recirculation (EGR) valve outer loop controllers are developed. The GPR feedforward models are trained with a series of mapping data with physically related inputs instead of speed and torque utilized in conventional control schemes. A physical model-free and calibratable controller structure is proposed for hardware flexibility. Furthermore, a discrete time sliding mode controller (SMC) is utilized as a feedback controller. Feedforward modeling and the subsequent airpath controller (SMC+GPR) are implemented on the physical diesel engine model and the performance of the proposed controller is compared with a conventional PID controller with table based feedforward.
DIESEL ENGINE AIRPATH CONTROLLER VIA DATA DRIVEN DISTURBANCE OBSERVER
Volkan Aran,Mustafa Unel 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.4
This study presents a novel data driven disturbance observer design method and its integration with a recursive controller developed for diesel engine airpath control problem. Mass Air Flow (MAF) and Manifold Absolute Pressure (MAP) are identified with Multi-Input Single-Output NFIR models using experimental data. Heavy-duty diesel engine airpath with variable geometry turbine (VGT) and exhaust gas recirculation (EGR) valve is the main focus of the paper. Presented data driven disturbance observer and controller are implemented to a serial production level engine embedded software. The proposed controllers are compared with the available commercial controllers in dynamometer test cycles. Better overall performance is achieved with respect to the commercial controllers using proposed easy-to-tune controllers.
OPTIMIZATION-ORIENTED HIGH FIDELITY NFIR MODELS FOR ESTIMATING INDICATED TORQUE IN DIESEL ENGINES
Gokhan Alcan,Volkan Aran,Mustafa Unel,Metin Yilmaz,Cetin Gurel,Kerem Koprubasi 한국자동차공학회 2020 International journal of automotive technology Vol.21 No.3
In this paper, optimization-oriented high fidelity indicated torque models which cover the whole operating regions under both steady-state and transient cycles for heavy-duty vehicles are developed. Two different experiments are performed and their data are merged to be utilized in the training of the models. In the first experiment, all combustion input channels are excited by quadratic chirp signals with different sweeps in their frequency profiles. Different from the first experiment, the engine speed is excited by ramp-hold signals in the second experiment. The estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer are utilized in the indicated torque calculations. In order to model the calculated indicated torque, a nonlinear finite impulse response (NFIR) model with a single layer sigmoid neural network has been designed. A sensitivity analysis is performed by generating several models with different number of input regressors and neurons. Experimental results show that the majority of the models in a selected wide range of the model parameters are validated with fit accuracies higher than 90 % and 85 % on the World Harmonized Stationary Cycle (WHSC) and the World Harmonic Transient Cycle (WHTC), respectively.