Fuzzy LQR-Based Control to Ensure Comfort in HVAC System with Two Different Zones

Heating, ventilation, and air conditioning (HVAC) systems are control systems that ensure indoor temperature and air quality meet desired conditions. In this study, a novel control strategy is proposed for an HVAC system operating under two distinct environmental zones with variable flow rates, addressing control challenges arising from external disturbances such as ambient temperature and humidity changes. In the system design, mathematical models were obtained, including the heat losses of two zones to the outdoor environment, as well as the heat transfer dynamics in the cooling unit, fans, and air ducts. For system control, considering ambient temperature, humidity, and variable flow rate, the required airflow was achieved by controlling the dampers placed in the indoor air inlet ducts. The core novelty of this work lies in the development and comparison of advanced control algorithms, including the Linear Quadratic Regulator (LQR), a Particle Swarm Optimization (PSO)-based LQR, and a newly designed PSO-based Fuzzy LQR (FLQR) controller. Comfort conditions were achieved by cooling the temperatures of two different regions from the ambient temperature to approximately 7 oC. The proposed FLQR controller combines the adaptability of fuzzy logic with the optimization capabilities of PSO to enhance system responsiveness and occupant comfort. Simulation results show that the FLQR method improves comfort performance by 90.4% for Zone-1 and 88.1% for Zone-2 compared to conventional LQR. The effectiveness of the proposed method (FLQR) is demonstrated through a comprehensive performance evaluation using Mean Squared Error (MSE) metrics, confirming its potential for intelligent HVAC applications.