Tension control of airline baggage labeling machine based on neural network PID

Yuyang Jin; Kai Li; Hantao Xiong

doi:10.1117/12.2645647

23 August 2022 Tension control of airline baggage labeling machine based on neural network PID

Yuyang Jin, Kai Li, Hantao Xiong

Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 123050I (2022) https://doi.org/10.1117/12.2645647
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

In order to improve the labeling effect and the work efficiency of the airline baggage labeling machine, the neural network is applied into the tension control for the tension fluctuation of the paper tape in the winding section. Firstly, the mechanism of tension generation is analyzed, and the mechanical device and dynamic model of tension control system in winding section are established respectively. Then combined with the coordinated control strategy, the simulation model of the winding section system of the labeling machine is built by Simulink. Finally, through the co-simulation of the winding segment model and the neural network controller, the control effect under the running state of the labeling machine is studied, and the comparison with the traditional PID control is analyzed. Compared with the traditional PID control, the simulation results show that the neural network PID has faster response time and smaller steady-state error , and has better dynamic performance. The stability control of the tension between paper tapes by the neural network algorithm is verified by simulation, which provides a theoretical basis for the continuity and accuracy of the subsequent labeling process and the guarantee of labeling quality.

Citation Download Citation

Yuyang Jin, Kai Li, and Hantao Xiong "Tension control of airline baggage labeling machine based on neural network PID", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 123050I (23 August 2022); https://doi.org/10.1117/12.2645647

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KEYWORDS

Neural networks

Control systems

Device simulation

Evolutionary algorithms

Computer simulations

Magnetism

Neurons

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