Coordinated Temperature and Humidity Control of Greenhouse Based on a Hybrid PID-MPC Strategy

Effective ventilation control is essential for maintaining an optimal greenhouse microclimate. However, conventional temperature-oriented control strategies often neglect humidity fluctuations under dynamic operating conditions, making it difficult to simultaneously satisfy crop thermal and humidity requirements. Based on a validated greenhouse simulation model, this study proposes a hybrid model predictive control (MPC)-proportional-integral-derivative (PID) control framework to achieve coordinated control of temperature and humidity in greenhouses. In the proposed architecture, PID-based control is used to regulate the ventilation mixing ratio, while MPC is employed to determine the total heating airflow. On this basis, a dynamic weight-based airflow allocation strategy is proposed to overcome the limitations associated with control objective switching in conventional PID control. The strategy optimizes indoor and outdoor airflow distribution by calculating the weights of key influencing parameters in real time. Comparative simulations were conducted for the coldest month and a typical month to evaluate system performance. Results show that, during the coldest month, the proposed weight control increased greenhouse temperature by 7.2% compared with temperature-focused PID control, while maintaining average relative humidity within 77.97%-78.52%. The weighted airflow allocation kept the indoor air volume fraction above 80% and improved the temporal stability of airflow regulation. In the typical month, optimized air allocation reduced the average reheat energy for outdoor air by 55.26%. Overall, the proposed hybrid control framework improved the adaptability and coordination of greenhouse temperature-humidity regulation under winter operating conditions.