Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models

Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin

Analysis on micro milling dynamics and stability

Smaller sizes are becoming more and more necessary for industry. Literally, machining features less than one millimeter are called micro machining. Milling using small or mini tools is one of the most common manufacturing processes for production of precision products. As in the macro milling, milling with mini tools also suffer from well known unstable vibration problem which is called regenerative chatter. Chatter prediction models need certain process, tool and workpiece related information. Tool tip frequency response function FRF is the key input information for cutting dynamics and chatter stability analyses. The common method for determining macro tool tip FRF is the experimental modal analysis. However, in micro tools receptance coupling analysis is popular in the literature due to certain restrictions of experimental test method. This thesis is focused on determining dynamic parameters of miniature milling tools by modal testing methods which are crucial to determine the stability characteristics of the micro flat end milling. An indirect modal testing method is presented. Also stability limit prediction with a conventional model is compared with experimental results. Various chatter detection methods and milling conditions are tested. In process chatter detection problems and modelling difficulties related with the miniature tool geometry are reported. Tool dynamics prediction is done with certain accuracy. Although the thesis study lacks from solid results in the stability limit prediction, discrepancy analysis of the test data are done and it is a first step for further studies

Data driven disturbance observer design and control for diesel engine airpath

Diesel engine airpath is a popular nonlinear control plant in the literature. Airpath is one of the key elements for engine out emissions control of the diesel engines. Its robust control requires controller design based on a nonlinear model of a system. Analytical model based control approaches are common in the literature. This study presents discrete sliding mode control and data driven disturbance observer design for diesel engine airpath. Identification tests and controller performance are simulated in a modeling environment where disturbance rejection capability of the developed control system is also characterized

Feedforward mapping for engine control

Feedforward control is widely used in electronic control units of internal combustion engines besides feedback controls. However, almost all feedforward control values are used in table form, also called maps, having engine speed and engine torque in their axes. Table approach limits all inte ractions in two input dimensions. This paper focuses on application of Gaussian process modelling of errors of inverse parametric model of the valve position. Validation results based on real engine data are presented for steady and dynamic conditions

Diesel engine NOx emission modeling using a new experiment design and reduced set of regressors

n this paper, NOx emissions from a diesel engine are modeled with nonlinear autoregressive with exogenous input (NARX) model. Airpath and fuelpath channels are excited by chirp signals where the frequency profile of each channel is generated by increasing the number of sweeps. Past values of the output are employed only in linear prediction with all input regressors, and the most significant input regressors are selected for the nonlinear prediction by orthogonal least square (OLS) algorithm and error reduction ratio. Experimental results show that NOx emissions can be modeled with high validation performance and models obtained using a reduced set of regressors perform better in terms of stability and robustness

Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements. A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steady-state experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-to-interpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively

Estimating soot emission in diesel engines using gated recurrent unit networks

In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay between the inputs and the output, and the number of regressors does not have to be chosen beforehand. Gates in a GRU network enable to capture such dependencies on the past input values without any prior knowledge. As a design of experiment, 30 different points in engine speed - injected fuel quantity plane are determined and the rest of the input channels, i.e., rail pressure, main start of injection, equivalence ratio, and intake oxygen concentration are excited with chirp signals in the intended regions of operation. Experimental results show that the prediction performances of GRU based soot models are quite satisfactory with 77% training and 57% validation fit accuracies and normalized root mean square error (NRMSE) values are less than 0.038 and 0.069, respectively. GRU soot models surpass the traditional NARX based soot models in both steady-state and transient cycles

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Hrant Dink'in katil zanlısı bir banka ile ayakkabı dükkânına ait kameralar sayesinde yakalandı. Hükümet sonucu Mobese'nin başarısı olarak göstermeye çalıştı. Yeni kameraların müjdesi verildi! Oysa Avrupa'daki uygulamaların sonuçları bu başarıyı yalanlıyor, kamera suçu önlemiyor, bireyin huzurunu bozuyor