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Injection control algorithm of diesel electronic control system based on neural network technology

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Abstract

With the continuous development of modern industrial control technology, neural network controllers have been used in many disciplines and various fields due to their simple structure and convenient adjustment of the parameters of each controller. In order to explore the current situation of the application of neural network technology to diesel electronically controlled injection control, In this paper, comparative experiment, the experimental data were collected using the initial model parameters and the comparative screening method, analyze several aspects of diesel engine improvement, and simplify The algorithm. And create an injection model that can use neural network technology. When re-adjusting the electronic control setting value of the diesel engine, the results show that the diesel engine is in the EC12.1-EC14.1 electronic control period. From the fuel consumption curve, it can be seen that the lowest fuel consumption rate is about 201 g/kWh, which appears in the electronic control function. The segment is at a temperature of 13.9. Conventional injection model was then compared with the improved nerve, conventional controllers set parameters: hq = 0.5, hu = 0.4, hw = 0.2; improved parameters: hq = 0.5, hu = 0.4, hw = 0.2, hx = 1.1. It is concluded that at 15 s, the improved type has too much adjusting power, which leads to the occurrence of overshoot, and it soon stabilizes at 17.1 s. The classic model adjustment has always been quite satisfactory, and there are no surprises. The speed of control is obviously inferior to the improved model. It is basically realized that starting from the convenience, efficiency and accuracy of the model, a neural network model that can control electronically controlled injection more quickly and effectively is designed.

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Funding

This work was supported by Characteristic innovative scientific research projects of colleges and universities in Guangdong Province (2019GKTSCX034), Guangdong science and technology innovation strategy special fund “climbing plan”(pdjh2021b0776).

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Authors and Affiliations

  1. College of Maritime, Guangdong Communication Polytechnic, Guangzhou, 510800, Guangdong, China

    Bing Gao & Shaoming Zhang

  2. Guangdong Ship Automation Engineering Technology Research Center, Guangzhou, 510800, Guangdong, China

    Bing Gao & Shaoming Zhang

  3. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhaoyang Li

Authors
  1. Bing Gao
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  2. Shaoming Zhang
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  3. Zhaoyang Li
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Correspondence to Bing Gao.

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Gao, B., Zhang, S. & Li, Z. Injection control algorithm of diesel electronic control system based on neural network technology. Int J Syst Assur Eng Manag 14, 613–625 (2023). https://doi.org/10.1007/s13198-021-01386-3

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  • DOI: https://doi.org/10.1007/s13198-021-01386-3

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