I + PD Temperature Controller Tuned for Ant Colony Optimization (ACO) on an ARM Platform

Montalvo, William; Guanochanga, Deisy; Chapaca, John

doi:10.1007/978-981-16-4126-8_22

Cited by 1 publication

(2 citation statements)

References 7 publications

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“…The quantization and scaling factors G e , G ec , G p , G i , and G d were added to the input and output ends of the fuzzy controller as the objectives of PSO optimization. The transfer function model of the Formula (6) was taken as the controlled object of the system, and Formulas (12) and (14) were taken as the system interference terms.…”

Section: Simulation Testmentioning

confidence: 99%

“…In recent years, there has been significant progress in the development of intelligent optimization algorithms, such as the genetic algorithm (GA) [7][8][9], particle swarm optimization algorithm (PSO) [10][11][12], and ant colony algorithm (ACO) [13,14]. As a result, a large number of scholars at both domestic and international levels have carried out extensive research on the use of these algorithms in temperature control.…”

Section: Introductionmentioning

confidence: 99%

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Temperature Regulation of Hot Vapor Preservation Treatment of Litchi Based on PSO-Fuzzy PID

Tang

Chen

et al. 2023

Applied Sciences

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In this paper, a control method was designed based on particle swarm optimization (PSO)–fuzzy PID (PSO-Fuzzy PID). In order to address the issues of significant nonlinearity and extensive hysteresis in the vapor temperature of litchi hot vapor preservation treatment equipment, as well as the inadequacy of traditional control schemes, a PSO-Fuzzy PID controller was developed through the integration of particle swarm optimization and fuzzy logic algorithms. This controller incorporates a scale factor and quantization factor, building upon the foundation of the Fuzzy PID controller. Additionally, a transfer function model was established for both the heating and disturbance links, utilizing a combination of test data modeling methods and relevant theory. Thirdly, a system simulation model was built to compare and verify PID, Fuzzy PID, and PSO-Fuzzy PID control methods, which were carried out on the MATLAB/Simulink simulation platform. Finally, the litchi load test was conducted to further verify the control performance of the PSO-Fuzzy PID method, where the litchi variety was “Jinggang Hongnuo”. The simulation results showed that the overshoot of the optimized control system was reduced by 93.5% and 42.9% compared with the traditional PID control and Fuzzy PID, and the regulation time was shortened by 46% and 31%, which indicates a better control effect. The test results showed that PSO-Fuzzy PID showed better system stability and accuracy than the fuzzy PID control method in different processing temperature and processing time for litchi’s hot vapor treatment. The maximum temperature difference decreased by 0.56 °C on average and the interference adjustment time was reduced by 7.5 s on average. PSO-Fuzzy PID has a good control effect on the temperature of litchi hot vapor treatment, which can provide a reference for model building and temperature control of other similar fruit and vegetable heat treatment equipment.

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Section: Simulation Testmentioning

confidence: 99%