Control algorithms for PSO PID iterative learning air-pressure simulation systems

Chunjun Chen; Xinbin Gong

doi:10.1117/12.3011362

1 December 2023 Control algorithms for PSO PID iterative learning air-pressure simulation systems

Chunjun Chen, Xinbin Gong

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 129401L (2023) https://doi.org/10.1117/12.3011362
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

In this paper, the typical pressure changes inside vacuum tank is, through a pressure control system, abstracted by means of a simulation of an airtight test device for a vacuum pipe train; Due to the characteristics of the pressure control system–repeated simulations of typical pressure changes in vacuum tank, and the difficulty in building accurate mathematical models, numerical models of the mass-pressure conversion of the system have been constructed; the pressure control of the system is also studied by PSOILC (the particle swarm optimization iterative learning control) algorithm in accordance with the system characteristics. Simulation results show that the PSOILC performs well in terms of convergence rate and control accuracy. In summary, this algorithm can effectively improve the control accuracy, convergence rate, and dynamic performance of the control system.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chunjun Chen and Xinbin Gong "Control algorithms for PSO PID iterative learning air-pressure simulation systems", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 129401L (1 December 2023); https://doi.org/10.1117/12.3011362

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