An adaptive approach to control a cooling coil chilled water valve operation, called adaptive fuzzy logic control (AFLC), is developed and validated in this study. The AFLC calculates the error between the supply air temperature and supply air temperature set point for air in an air handling unit (AHU) of a heating, ventilating, and air conditioning (HVAC) system and determines optimal fuzzy rule matrix to minimize the hydronic energy consumption while maintaining occupant comfort. The AFLC uses genetic algorithms and evolutionary strategies to determine the fuzzy rule matrix and fuzzy membership functions for an AHU in HVAC systems;Cooling coil models are developed using neural network, general regression neural network and lump capacitance methods to predict the supply air temperature. These models helped with the development of the adaptive fuzzy logic controller;Two types of validation experiments were conducted, one with cyclically changing supply air temperatures and the second with cyclically changing supply air flow rates. Experiments conducted on two identical real HVAC systems were used to compare the performances of the AFLC to a conventional proportional, integral and derivative (PID) controller. To remove bias between the testing systems, the controllers were switched from one system to the other;The validation experiments indicate that the HVAC system operated under the AFLC consumes 1 to 7 % less hydronic energy when compared with a conventional PID controlled system. More actuator travel distance was observed when using the AFLC. The AFLC maintained better occupant comfort conditions when compared with the conventional PID controller. It was observed that the controlled variable for the AFLC system required 0 to 185% more rise time, had 9 to 68% less overshoot and required 11 to 45% less settling time as compared to the conventional PID controlled system.