This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Study of Advanced Control Based on the RBF Neural Network Theory in Diesel Engine Speed Control
ISSN: 1946-3936, e-ISSN: 1946-3944
Published October 14, 2019 by SAE International in United States
Citation: Zhao, G., Long, Y., Ding, S., Yang, L. et al., "Study of Advanced Control Based on the RBF Neural Network Theory in Diesel Engine Speed Control," SAE Int. J. Engines 13(1):63-75, 2020, https://doi.org/10.4271/03-13-01-0005.
Based on radial basis function (RBF) neural network (NN) theory, RBF-Proportional Integral Derivative (PID) diesel engine speed control is proposed. The algorithm has strong self-learning ability and strong adaptive ability, and is able to optimize the control parameters of the speed loop controller in real time. A series of simulations are carried out with different initial weights. Simulation results reveal that initial weights have little effect on RBF-PID control performance. A STM32 MCU-based controller is developed according to the calculation requirement. Experiments are carried out on a D6114 diesel engine generator to verify the proposed speed control algorithm. The simulation results are in agreement with the experimental results. The results show that the influence of initial weights on RBF-PID control algorithm is smaller than that on BP-PID control algorithm. When RBF-PID control algorithm is adopted, the steady speed fluctuation rate is 0.4%. When sudden load is carried out, the speed recovery time is 2.1 s and the instantaneous adjustment rate is 4.93%. When sudden unload simulation is carried out, the speed recovery time is 2.2 s and the instantaneous adjustment rate is 5.27%. Speed control performance of diesel engine has been greatly improved.