Intelligent Control Technology of Electric Pressurization Based on Fuzzy Neural Network PID

In this study, we delved deeply into the intelligent control technology of electrical pressurization, utilizing a fuzzy neural network-based PID approach. By meticulously crafting a fuzzy neural network model and optimizing the PID control algorithm, we achieved intelligent control of electrical pressurization systems, enhancing both system stability and response speed. The findings of our thorough data analysis are highly significant, indicating that this technology has achieved exceptional outcomes in practical applications. This paper delves into a comparative analysis of the performance between intelligent electrical pressurization control utilizing a fuzzy neural network PID and conventional control methodologies. Under the conventional approaches, voltage standards exhibited a deviation of 2.5% along with a fluctuation span that reached as high as 5%. However, with fuzzy neural network PID control, voltage standards were narrowed to a deviation of 1.5%, with a fluctuation range reduced to 3%. Additionally, the conventional control method necessitated a duration of 15 seconds to attain a stable condition, whereas the fuzzy neural network PID control method effectively minimized this time requirement. In this study, the system stability and response speed were improved by optimizing the PID algorithm by using a fuzzy neural network model. Comparative analysis shows that our method reduces the voltage deviation from 2.5% to 1.5% and reduces the fluctuation range from 5% to 3%. It reaches steady state in 8 seconds and reduces energy consumption by 20% compared to the 15 seconds of the conventional method. The results show a significant improvement in practical applications. Compared with traditional control methods, this technology has significantly improved stability, response speed and energy consumption.

@article{Li2024,
  title     = {Intelligent Control Technology of Electric Pressurization Based on Fuzzy Neural Network PID},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yabing Li and Limin Su and Huili Guo},
  doi       = {10.14569/IJACSA.2024.01509102},
  url       = {https://doi.org/10.14569/IJACSA.2024.01509102}
}