RBF Neural Network-Based And Inverse Model Control For Transient AFR In Gasoline Engine

Decreasing emission and fuel consumption of automobile is an international issue about environmental protection and source of energy.To control the transient AFR(air-fuel ratio) of gasoline engine in high precision is an important measure to solve this issue, but it is a difficult and challenging subject which is well known of the world. Aiming to this subject, the author develops RBF(radial basis function) neural network and adverse model and applies it to control AFR of gasoline engine in this paper. The author then emulates the two controllers off-line under MATLAB software platform and emulates it on-line under SIMULINK software platform based on mean engine model.The author begins with the necessity and importance of AFR control(espe-cially the transient AFR) in automobile engine and researches on this issue that have been done both domestically and abroad. Then, a method which chooses a model under the different strategy of control is gotten based on the synthetically analyzing of engine models that have existed till now.Subsequently, the author adopts neural network and adverse model to be the controller of gasoline engine as the result of analyzing existed strategy. Furthermore, the author analyzes these neural networks that have been used in control area respectively and get the result that RBF neural network is a very good choice for AFR control of gasoline engine. The strategy that is adopted to control AFR is brought up by the author and nothing is found in all publications about this area. Knowledge which is related to neural network and adverse control is introducednecessarily in this paper.The gasoline engine model which was brought up by professor Elbert Hend-ricks and his colleague who works in Denmark technology university is adopted in this paper. This engine model has very high precision and its standard error is only 2-3% in whole working range for three different engine models. It has the similarly precision level to the same engine adopting different manifold and fuel injection system. So it is adopted widely.After engine model and its control strategy have been established , the author compiled MATLAB programs about RBF neural network and adverse model and emulated them off-line. In the meanwhile, a kind of adaptive compensation algorithm was added in the programs in order to constitute adaptive RBF neural network and adaptive adverse control system. Both simple fuel film of engine and complicated one have been emulated and these two kinds of control strategy all get very good result. Subsequently, analysis and comparison are accomplished between this result and the result which is gotten by CMAC neural network.Ultimately, the author uses S_function to express controllers of these two control strategy under SIMULINK software platform, so the SIMULINK emulation on-line model based on S_function is accomplished. Two controllers' robustness and adaptability are tested by changing load^ parameter of fuel film> replacing function of fuel film and adding random noise in ignition angle in the SIMULINK emulation model. It is showed that both error is inside ± 2% between ideal AFR and AFR which is gotten using these two controllers, and both two controllers have good robustness. In the end , all kinds of results are analyzed synthetically, compared with the result which is gotten by CMAC neural network controller.There are 67 references, 80 figures and 9 tables in this paper.