As a more efficient power machine, diesel engine is widely used in industry, large vehicles, ships, power generation and other industries. Because of its high thermal efficiency, low fuel consumption, strong power and long service life, diesel engine will continue to occupy a leading position in its application field in the coming decades. Therefore, the speed control of diesel engine has always been a hot research topic. However, there are some disadvantages if only using PID controller for diesel engine speed control. This paper studies the structure of diesel engine speed control and the form of neural network. The combination of BPNN (Back Propagation Neural Network) and PID control makes it adjust the PID parameters in real time according to the speed error and achieve the tracking of actual speed and ideal speed. The realization of this method solves the problem of difficult PID parameters selection to a certain extent. And it has the characteristic of automatically adjusting parameters under variable working conditions. So that the engine can still maintain the desired speed under variable working conditions.

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