# Temperature Regulation of Hot Vapor Preservation Treatment of Litch Based on PSO-Fuzzy PID
## 6 Conclusions  
This paper presents a PSO-Fuzzy PID control method for optimizing the temperature control in litchi hot steam preservation treatment. The control method is designed by combining the technology flow and equipment, temperature control method, and optimization algorithm theory. The PSO-Fuzzy PID controller parameters are optimized offline using particle swarm optimization. The litchi steam treatment equipment operation process is analyzed, and the transfer function is modeled using experimental and mathematical analysis methods. A control system simulation model is built on the MATLAB/Simulink platform, where the performance of PID, Fuzzy PID, and PSO-Fuzzy PID control methods is compared. The PSO-Fuzzy PID method reduces overshoot by 93.5% and shortens the adjustment time by 46% compared to the PID method. Furthermore, the PSO-Fuzzy PID method shortens the time of interference adjustment by 31% when compared to the Fuzzy PID method, resulting in improved stability, accuracy, and rapidity of the system. Finally, load tests are conducted using \"Jinggang Hongnuo\" litchi for hot steam treatment, and the results show that the optimized PSO-Fuzzy PID method decreases the maximum temperature difference by 0.56 \({}^{\circ}\)C and the interference adjustment time by 7.5 s on average.  
Compared to other traditional control methods, using intelligent algorithm optimization can make the system adjustment process more convenient. On this basis, the accuracy of system parameters can be improved, which further enhances the control effect. These findings are relevant for temperature control of other fruit and vegetable heat treatment equipment.  
Conceptualization, H.T.; methodology, H.T.; software, H.T.; validation, H.T. and L.H.; formal analysis, H.T.; investigation, R.J.; resources, R.M.; data curation, R.J.; writing--original draft preparation, H.T.; writing--review and editing, R.M.; visualization, H.T.; supervision, Y.C.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (grant number 2021B1212040009); and the APC was funded by Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (grant number 2021B1212040009).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Evaluation** \\ **Indicators** \\ \end{tabular} } & \multicolumn{3}{c}{**Processing Parameters**} & \multicolumn{3}{c}{**Control Method**} \\ \cline{2-7}  & **20 s** & **30 s** & **80 \({}^{\circ}\)C** & **85 \({}^{\circ}\)C** & **Fuzzy PID** & **PSO-Fuzzy PID** \\  & **(a/b/e/f)** & **(c/d/g/h)** & **(e/f/g/h)** & **(a/b/c/d)** & **(a/c/e/g)** & **(b/d/f/h)** \\ \hline MTD & \multirow{2}{*}{
\begin{tabular}{c} 1.665 \\ (Average)/\({}^{\circ}\)C \\ \end{tabular} } & \multirow{2}{*}{2.055} & \multirow{2}{*}{1.97} & \multirow{2}{*}{1.75} & \multirow{2}{*}{2.14} & \multirow{2}{*}{1.58} \\  & & & & &