An improved SABO-optimized interval type-2 fuzzy logic controller for HVAC systems

Heating, ventilation, and air conditioning (HVAC) systems account for a substantial proportion of global energy consumption. While numerous control strategies have been developed to reduce energy consumption, conventional methods fail to balance energy efficiency and thermal comfort effectively due to the inherent nonlinearities, time-varying dynamics, and multivariable coupling effects of HVAC systems. Although interval type-2 fuzzy logic controllers (IT2 FLCs) can mitigate such uncertainties, their performance is critically dependent on the optimal configuration of membership function (MF) parameters, which conventional methods often fail to achieve. To address these issues, this paper proposes an HVAC-adapted IT2 FLC and develops an improved subtraction-average-based optimization algorithm (ISABO) with adaptive mechanisms to enhance MF parameter tuning. Benchmark tests demonstrate that ISABO achieves faster convergence and higher solution accuracy than the original subtraction-average-based optimization algorithm. The results of the simulation experiments verify the superiority of the proposed method: temperature tracking errors are reduced to near-zero values, humidity ratio deviations decrease to 2.8%, and energy consumption is lowered by 22.8% compared with conventional fuzzy controllers.