@article{Li2026, 
author = {Yameng Li and Shan Li and Heng Wang and Zhanwu Wang and Zhenfeng Wang},
title = {Adaptive temperature control of refrigerated vehicles based on ISSA-GA optimized fuzzy PID with synergistic feedforward compensation},
year = {2026},
journal = {Transactions of the Chinese Society of Agricultural Engineering},
volume = {42},
number = {15},
pages = {376-388},
keywords = {LTL logistics, temperature control, fuzzy PID control, feedforward compensation},
url = {https://www.sciopen.com/article/10.11975/j.issn.1002-6819.202601056},
doi = {10.11975/j.issn.1002-6819.202601056},
abstract = {Refrigerated vehicles have been widely used for less-than-truckload (LTL) cold chain logistics in recent years. However, the refrigeration system is confined to the large inertia, time delay, and nonlinear signals, and temperature fluctuations caused by frequent compartment door openings during multi-stop urban distribution. In this study, a temperature control system was developed for the high-precision adaptive temperature and the quality of fresh food transportation. A dynamic heat exchange model was established for the refrigerated compartment, according to energy conservation and heat transfer. The respiration heat was also considered, such as heat leakage from the enclosure, and thermal disturbances during compartment door openings. An Improved Sparrow Search-Genetic Algorithm (ISSA-GA) was proposed to combine the strong global exploration of the Sparrow Search Algorithm (SSA) with the crossover and mutation operators in the Genetic Algorithm (GA) for discrete variable optimization. The t-distribution mutation was introduced to enhance the global search, while the dynamic-radius perturbation realized a smooth transition between exploration and exploitation, thereby achieving efficient collaborative optimization of fuzzy PID membership functions. Furthermore, feedforward-feedback control strategy was designed to monitor compartment door in real time and then trigger feedforward compensation. The convergence curves demonstrated that ISSA-GA exhibited superior global search. Standard GA stagnated after the 45th generation, whereas the hybrid algorithm achieved global convergence in 73rd generation. According to the Integral of Time-weighted Absolute Error (ITAE), the final fitness value was improved by 18.02% and 1.95%, compared with GA and standard SSA. Simulations and experiments were validated using MATLAB/Simulink and a 50 L scaled-down trial chamber. The results showed that the settling time of ISSA-GAFuzzy controller was 60.5 s, which was reduced by 62.54% and 59.40% compared with the conventional PID and fuzzy PID controllers, respectively. The maximum overshoot was 0.69%, which was reduced by 94.90% and 93.02%, respectively, compared with the conventional PID and fuzzy PID controllers, respectively. Temperature fluctuation was reduced from 2.26 to 0.98 ℃ during compartment door opening after feedforward compensation. Continuous temperature setpoint experiments demonstrated that the smooth transitions were achieved under both small and large setpoint variations, particularly without overshoot for rapid stabilization. Experimental verification showed that the temperature tracking rates reached 83.2% and 85.14%, respectively, under setpoints of 5 and 0 ℃. The maximum temperature error was within 0.26 ℃, indicating a low steady-state fluctuation coefficient. Door-opening disturbance rejection experiments further verified that there was the high effectiveness of feedforward compensation. The feedforward-enhanced ISSA-GAFuzzy controller achieved a disturbance recovery time of 110.80 s, which was 43.61% shorter than that without feedforward compensation. Meanwhile, the temperature rise amplitude was reduced by 42.86%. The better performance of temperature control system was achieved in response speed, steady-state control accuracy, and disturbance rejection robustness. The findings can provide a reliable technical solution for fresh food cold chain logistics.}
}