scope:

Humidity is an important factor contributing to one 's thermal sensation and comfort. It also affects one's perception of the air quality. Air-conditioning systems often encounter humidity problems at low cooling load. This paper proposes an intelligent controller for an air-handling unit to control the temperature while limiting the humidity below 70%. The proposed scheme is based on the back-propagation-through time approach. It uses artificial neural networks to develop an emulator to learn on-line the plant dynamics and a controller to control the fan speed and chilled water valve opening in real time. The neural-based controller was implemented on an industrial air handler for performance validation purposes. The implementation results show that the intelligent controller could effectively control the temperature and humidity within the operating range investigated. The results also indicate the potential of intelligent controllers as practical alternatives for controlling nonlinear and complex air-conditioning systems.