Research On Intelligent Control For Rail Pressure Of High Pressure Common Rail Diesel Engine

The high pressure common rail diesel engine is a trend of the development of diesel engines in the future, meanwhile, it is a complicated, nonlinear and time-variant system, which makes the control of diesel engine more difficult, leading to a high request of the control result. In other words, the selection and improvement of the control mode of the high pressure common rail diesel engine become a key factor of enhancing the performance of high pressure common rail system.The traditional PID is widely applied in the control of industrial process due to its simple structure, parameters with clear physical meaning and some robustness, however, it exists the shortcomings of fixed control parameters and inferior real time nature. Compared with PID, the intelligent controlling means shows stronger function of self-learning, self-adapting and self-organizing and better fault-tolerant ability. In this paper, an intelligent control technology-was used to make up for the lack of PID, and the application of intelligent controlling means on high pressure common rail diesel engine was explored.After learning kinds of intelligent control methods, the Genetic BP neural networks PID control algorithm is proposed in the rail pressure control of the high pressure common rail diesel engine, and the Genetic algorithm and BP neural networks are introduced to optimize PID control parameters. The construction of BP neural network and the operation of Genetic algorithm are elaborated in detail, then the Genetic BP neural networks is realized under MATLAB environment. To verify the control effect of the Genetic BP neural networks PID control algorithm, the high pressure common rail diesel engine model is built under Matlab/Simulink environment. A debugged GA-BP-NN algorithm is used to generate Simulation module, the control model of high pressure common rail diesel engine is built and the simulation study is also proposed. The simulating results indicate that the algorithm based on Genetic BP neural networks PID is able to improve each performance index of the diesel engine control system. Therefore, the combination of intelligent controlling means and the traditional PID is effective and feasible.