Hybrid Control Strategies for Greenhouse Climate Regulation: PID, Fuzzy, and Neuro-Fuzzy Comparative Implementation in Temperate-Dry Crop Systems
Resumo
This paper presents a comparative analysis of three control strategies—PID, Fuzzy Logic, and Neuro-Fuzzy—for thermal regulation in a greenhouse cultivating tomatoes, peppers in Guaslán Grande, Ecuador. The experimental setup utilizes the Libelium Wasp-mote Pro embedded platform equipped with Smart Agriculture Xtreme sensors, enabling real-time monitoring of four critical environmental variables. A first-principles thermal model was developed and validated to guide the design and tuning of each controller. The PID controller was tuned using a Cohen-Coon method, the Fuzzy controller implemented 45 adaptive Mamdani-type rules, and the Neuro-Fuzzy controller combined a three-layer neural network with Takagi-Sugeno inference, trained via backpropagation. Experimental results showed the Neuro-Fuzzy controller achieved the lowest mean absolute error (0.51 ± 0.03°C), though these differences were not statistically significant (p > 0.05). Neuro-Fuzzy also led to a 63% reduction in integral squared error and 57% faster settling time compared to PID, with nearly identical average power consumption (0.2% difference). The system maintained internal temperature within ± 0.5°C, operated autonomously on lithium-ion batteries with OTA updates, and demonstrated that low-cost embedded systems (0.15 /m2)areviable, scalablealternativestoindustrialplatforms(1.2/m2) for precision agriculture in developing regions. The integration of physical modeling, intelligent control, and advanced sensing via Wasp-mote Pro enables efficient and adaptable climate management, contributing to sustainability and resource optimization in agricultural systems.
