Neural modelling and control of a Diesel engine with pollution constraints
نویسندگان
چکیده
Abstract: The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural sub-models, each of them constituting a nonlinear Multi-Input Single-Output Output Error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can describe or control accurately complex nonlinear systems, with few a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.
منابع مشابه
Modeling of Combustion and Carbon Oxides Formation in Direct Injection Diesel Engine
When looking at the effects of diesel engine exhaust on the environment, it is important to first look at the composition of the exhaust gases. Over 99.5% of the exhaust gases are a combination of nitrogen, oxygen, carbon dioxide, and water. With the exception of carbon dioxide, which contributes about 5% of the total volume, the diesel engine exhaust consists of elements which are part of...
متن کاملInvestigation of performance and exhaust emission of diesel engine in different ratios of lambda coefficient using diesel-ethanol fuel blends
In recent decades, rising greenhouse gas emissions have become a major challenge and concern. Environmental pollution is one of the greatest human challenges of the 21st century. Improving the performance of internal combustion engines and reducing the amount of pollutant emissions has always been a concern for researchers. The use of alternative fuels and the control and adjustment of the lamb...
متن کاملArtificial neural network model to predict the performance of a diesel power generator fueled with biodiesel
Alternative fuels are intensively investigated for the replacement of the diesel fuel. Today the diesel power generators are mostly used in the various industrial companies in Iran. Therefore, it is necessary to estimate the level of performance of the diesel power generators fueled with biofuels. For the first time, in this study, the prediction of the performance of a diesel power generator m...
متن کاملArtificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine
In this study the performance and emissions characteristics of a heavy-duty, direct injection, Compression ignition (CI) engine which is specialized in agriculture, have been investigated experimentally. For this aim, the influence of injection timing, load, engine speed on power, brake specific fuel consumption (BSFC), peak pressure (PP), nitrogen oxides (NOx), carbon dioxide (CO2), Carbon mon...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of Intelligent and Robotic Systems
دوره 41 شماره
صفحات -
تاریخ انتشار 2005