Greenhouse control systems are extensively used in the production of crops. However, temperature and humidity control pose the problems of multivariate coupling, nonlinearity, and high inertia. In this study, a multivariate decoupling fuzzy proportional-integral-derivative (PID) control model based on genetic algorithm (GA) optimization was developed. First, by using the dynamic mathematical model of the greenhouse control process, the coupling between temperature and humidity was reduced by decoupling the greenhouse model by using a multivariate pre-feedback decoupler. Next, GA was used to optimize the quantitative and proportional factors in fuzzy PID controllers to determine the global optimal value. Finally, the designed control model was simulated in MATLAB/Simulink. The output of the GA-optimized temperature controller was 98.87% lower than that of the unoptimized temperature controller. Upon optimization, the humidity controller output, temperature transition time, and humidity transition time were 89.67% 16.67%, and 38.46% lower than when not optimized, indicating increased system stability. Therefore, the proposed model effectively improves the control accuracy of greenhouse temperature and humidity.
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