Simulation Analysis of Intelligent Control System for Excavators in Large Mining Plants Based on Electronic Control Technology

Abstract: With the increasing demand for large-scale mine equipment and the complexity of the operating environment, the intelligent trajectory planning and control of mine systems becomes very important. This paper proposes a proportional-integral-differential (PID) feedback controller combined with adaptive improvement. This controller combines Genetic Algorithm and Particle Swarm Optimization technology to enhance the ability of the excavator’s intelligent control system and improve the control accuracy, response speed, and robustness under different working conditions. The results showed that the constructed PID controller improved the average constraint performance by 2.5% through quintic interpolation, and the power consumption was relatively small. The trajectory prediction error of different joints was less than 5% and the displacement and pressure curves of the hydraulic cylinder were stable and symmetrical. The accuracy of the proposed algorithm was 94% and quickly converged to 0.05 after 50 iterations, which was 18.5%, 15.3%, and 17.5% higher than the other three algorithms, respectively. Therefore, the proposed method has high reliability and adaptability in anti-interference ability, trajectory planning progress, and optimization efficiency, and it provides a better solution for intelligent control of the excavator excavation system.

Lei Sun. “Simulation Analysis of Intelligent Control System for Excavators in Large Mining Plants Based on Electronic Control Technology”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.11 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0151176

@article{Sun2024,
title = {Simulation Analysis of Intelligent Control System for Excavators in Large Mining Plants Based on Electronic Control Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151176},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151176},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Lei Sun}
}