The temperature and humidity control processes in a variable-speed direct-expansion air-conditioning (DX A/C) system are significantly coupled

which makes it difficult to realize the precise control of indoor air temperature and humidity simultaneously when traditional methods are used. Based on a fuzzy proportional-derivative (PD) control logic and steady-state artificial neural network (ANN) model

a novel temperature and humidity simultaneous control strategy was developed in this study. This control strategy uses the updating temperature and humidity errors to calculate the actually required control signals

the compressor

and the fan speeds to achieve simultaneous control of the temperature and humidity. Controllability tests were carried out to examine the performances of the novel controller including command following tests and disturbance rejection tests. The command following test results showed that the air dry-bulb and wet-bulb temperatures can be controlled to their new set points within 720 s with an oscillation of no more than ± 0.2 ℃ after the set points are changed. The disturbance rejection test results showed that

when a disturbance is introduced into the cooling load

the controller can respond immediately if the temperature differences between the set points and the present values reach 0.5 ℃ and maintain the dry-bulb and wet-bulb temperatures at their set points within 600 s

with a moderate oscillation of ± 0.2 ℃. Thus

the experimental results suggest that the novel control strategy established in this study can realize the simultaneous control of the indoor air temperature and humidity using a variable-speed DX A/C system.