Study of Neural Network Control Algorithm in the Diesel Engine

Paper #:
  • 2016-01-8086

Published:
  • 2016-09-27
DOI:
  • 10.4271/2016-01-8086
Citation:
Song, E., Liu, J., Ding, S., Wang, Y. et al., "Study of Neural Network Control Algorithm in the Diesel Engine," SAE Technical Paper 2016-01-8086, 2016, https://doi.org/10.4271/2016-01-8086.
Pages:
9
Abstract:
Based on BP neural network theory, a BP-PID control algorithm with strong self-learning and self-adapting ability is designed for the diesel engine speed governor. Nonlinear continuous functions can be approached with high precision by using this algorithm. The parameters of speed loop controller can be calibrated in real time through the BPPID algorithm. In order to verify the advantages of BP-PID control algorithm in reducing overshoot, increasing diesel engine dynamic characteristics and resisting disturbance, simulation model is built and experiments are carried out under initial condition, steady condition and condition with sudden load change. We compare the simulation results and the experiment results, and find they match each other. The results indicate that the transient speed regulation of the diesel engine can meet the requirements of stage power station by using BP-PID control algorithm. The BP-PID control algorithm has advantages over traditional PID control algorithm in maintaining engine stability.
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