Aşırı doldurmalı ve egzoz gaz çevrimli dizel motorda model öngürülü kontrol

In the 21th century, despite the research in electric and hybrid cars, internal combustion engines still have the domination. However, todays's combustion engines differ a lot from their old ancestors due to stringent emission regulations which forces automotive industry to design green engines. Tighter emission regulations caused the downsizing trend which made the turbocharger is a must in the new engine familes. On the other hand, diesel engines with turbochargers has been drawn a lot attention due to its great fuel economy, strong low end torque and better compression ratio. In order to deal with famous emission phenomena, NOx – PM trade off, of the diesel engines, exhaust gas recirculation systems has been designed reduce NOx emissions. Therefore, the diesel engine to be focussed on this study, have the two fundamental technology: turbocharger and exhaust gas recirculation, which projects the almost all diesel engines currently in the automotive industry. Turbocharger system works with a compressor in the intake and a turbine in the exhaust port which are coupled to each other via shaft. When the energy due the exhaust flow throught the turbine is transferred to the compressor via shaft and this provides the power for the compression of intake air by just using waste exhaust gases. Moreover, compression of intake air incresases the air density for the same volume so incresas the volumetric efficiency of the combustion chamber. This is the key element which initites downsizing era in the internal combustion engines. On the other hand, reason of the exhuast gas recirculation system is completely to reduce NOx emissions. Exhaust gas recirculation system returns the some part of exhaust flow before turbine, then cools it down and blows it to the intake manifold. Mixture of fresh air with the burned air results less oxygen in the air to be combusted. Thus, peak combustion temperature, which is strictly depends on oxygen concentration and the main reason of NOx production, is reduced. In summary, modern diesel engine has a complex air path structure due to turbocharger and exhaust gas recurculation. As a result, control of the flow and pressure of intake air mass which will be sucked into combustion chamber, becomes complicated. This study investigates different approaches to the control of air path as model predictive control and compares it with the control algorithm currently being used. Developing control oriented diesel engine models is not a new topic and under investigation for the last twenty years. Mean value engine modeling is one of the most popular and accepted method to do so. However, creation of diesel engine model is a longlasting and diffuclt process due to its complex, non-linear equations and the differential equations. This study offers an easier method for the modeling which is the use of AVL Boost RT. The software works as component based modeling logic and each possible transfer is handled differently as mechnanical, heat or flow. To reach a accurate diesel engine model, it is only necessary to parametrize each component to be modeled. Then Boost RT handles all the solution of differantial equations and the physical phenomenas. The use of modeling software gives the great advantage of work with complex systems rather easier to the control society. Another big advantage is that once the diesel engine model is ready, it can be imported to Matlab Simulink to design and simulation of the controllers. The diesel engine models desingned in Boost RT, is quite flexible and model is easy to convert or adapt to the different engines or different designs. Diesel engine air path as a system itself should have two inputs, which are turbocharger vane position and exhaus gas recirculation valve position, and two outputs, which are manifold absolute pressure (MAP) and mass air flow (MAF). So, this is a multi input multi output system which is strongly coupled. For example, openin further of the exhaust gas recirculation will cause reduction in fresh air so mass air flow plus it will also reduce the manifold absolute pressure by diverting some of the exhaust gases which drives the turbine. However, in the automotive industry, the general control approach the diesel engine air path is using two seperate single input single output systems despite the explained coupled behaviour. In order to get good set point tracking and disturbance rejection, there are too many parameters to be calibrated for each PID controller of the air path. For example, P, I and D term have all different 2D interpolation maps which depends on the error and engine operating point in terms of engine speed and load. Additionaly, open loop feedforward structure has 2D interpolation look up tables to be filled. On top of that in order to cope with system non-linearites, there are some special functions to be calibrated as rata limitation, dynamic response, output hysteresiz. Finally, despite all the calibration efforts, overall controller response of diesel engine air path has a lot of overhsoots, undershoots and short term instabilities. Calibration of the PID controllers for the air path system, Ziegler Nichols osscilation method is being used. Controllers without feedforward control would give a poor performance. This has been proved by the addition of feedforward term to the main controllers. In addition to that, new calibration method for the air path controllers are suggested as the use of genetic algorithm. Tuning controllers may take too much time with genetic algorithm but the output is much more succesfull than the ones tuned by Ziegler Nichols. On the other hand, feedforward approach does not give much to the controller tuned by genetic algorithm. Hence, this raise a new approach by removing feedforward terms which would reduce system complexity and reduced time. At last, model predictive controller was designed to control diesel engine air path. Model predictive control has a natural advantage to cope with multi input multi output system due to its quadratic problem solving method. Besides, it handles with system constraints very well and once the plant model is designed, rest of the calibration action is just adjusting scaling factors to prioriteze outputs. On the other hand, plant model may be created with various methods but it needs to be linear. In this work. Matlab system identification toolbox was used. Direct two input two output state spce model of the system was used. The plant model is imported to the model predictive control block in Simulink. Then the controller is tuned in terms of prediction horizon, constraints and scaling factors. Prediction horizon is the number of samples that the plant model simulated to get future plant responses. System constraints are the maximum and mimum position of the actuators, actuators controller step rates and physical limits of mass air flow and manifold absolute pressure. In short, model predictive controller gave a better performance than the any other controllers which is a promising result. During this work, for the engine model Boost Rt and the rest of the calculations Matlab Simulink and its system identification, model predictive control and parameter estimation toolbox were being used. The algorithms work behind this elements were shared to provide theoretical background. In the last chapter possible further steps were disscussed.