Temperature and humidity control with a model predictive control method in the air-conditioning system

Indoor air environment is usually controlled for occupants comfort with an air-conditioning (A/C) system. A model predictive control (MPC) method was proposed in this paper in order to control the air temperature and humidity which are coupled in the A/C system. Based on a multivariable control-oriented model, a controller was designed with a cost function. The control variables for the next control cycle were calculated by optimizing the cost function over a prediction horizon of 20s with a particle swarm optimization (PSO) algorithm. Different operation conditions of indoor air temperature and humidity were simulated in MATLAB to test the effectiveness of the control method. A PID control method was also used for the A/C system and simulated for comparison. Results showed that, the indoor air temperature and humidity could be well controlled with the proposed MPC method in the whole system operation range, and the MPC method showed better control performance than the PID control method.