Design and Validation of a Mamdani-Type Fuzzy Inference System for Dynamic Indoor Climate Balancing

Abstract

The goal of this research is the design, simulation, and validation of a stable and energy-efficient Mamdani-type-1 FLC that controls an indoor climate balancing system by overcoming the drawbacks of conventional linear control in handling the intrinsic nonlinearity and complexity of the system. The main objective will be to dynamically control crucial climate parameters such as Fan Speed and Cooling Rate based on crisp input values of Temperature in the range [8 44] and Relative Humidity in the range [0 90]. The operational intelligence of the FLC relies on a comprehensive fuzzy rule base of thirty-five (35) IF-THEN rules that connect seven fuzzy sets for temperature and five for humidity to their corresponding output actions. The simulation also highlights the capability of the FLC to smoothly offer nonlinear control transitions from minimum to maximum effort, thus avoiding abrupt on/off behavior that wastes energy. This research validates the FLC as an effective, feasible, and energy-efficient control solution, laying a very firm foundation for further research.