Fuzzy greenhouse climate control

The first version was a MATLAB Mamdani controller that turned a sensed and a target temperature into two PWM signals, one for a heater and one for a cooler. Fuzzy logic suits this job because it works with terms such as “cold”, “warm” and “hot” the way an operator would, and moves smoothly between them.

Version 2 keeps fuzzy logic at the core and builds an industrial system around it.

What version 2 adds

• A redesigned controller. An incremental fuzzy-PI with a 7 × 7 rule base that works on the error, so one rule base serves every crop and setpoint.
• Split-range actuation. Heating, free cooling through vents, then pad or compressor cooling, with no heating and cooling at the same time.
• A safety supervisor. Redundant sensor validation, frost and heat limits, interlocks and a fail-safe mode.
• A digital twin. A grey-box thermal model with solar gain, crop transpiration and realistic sensors with injectable faults.
• Model predictive control that uses weather forecasts, with fuzzy-PI as fallback.
• Anomaly detection that finds drifting, frozen or spiking sensors and removes them from the fusion.
• Field integration. A ROS 2 package, a Modbus TCP link to a PLC, MQTT telemetry, and export of the controller as IEC 61131-3 Structured Text or a C99 header.

Results on the digital twin

Fuzzy-PI moves the actuators about 2.5 times less than a tuned PID for nearly the same tracking, which means less wear on valves, burners and compressors. These numbers come from the digital twin, not from a real site.