Research on Multivariable Coordinated Predictive Control Algorithm Based on Air Conditioning Controller

In view of the significant hysteresis, nonlinearity, and time-varying characteristics of traditional air-conditioning systems, intelligent air-conditioning systems are evolving towards enhanced controllability, multifunctionality, and reduced power consumption. This paper proposes a multivariable coordinated predictive control algorithm applied to air-conditioning controller systems. The algorithm's principles are analyzed by examining input variables and set temperatures, such as temperature, humidity, electrical parameters of air conditioning operation, flammable gas concentration in the environment, ionic smoke detection status, and personnel mobility. Output variables such as humidity and its deviation are also analyzed, and the design steps of the multivariable coordinated predictive control algorithm applied to the PC server software of the air-conditioning system are presented. Finally, a comparative test of temperature and humidity control was conducted on a single Midea air conditioner. The results indicate that the multivariable coordinated predictive control algorithm is well-suited for air-conditioning control systems, particularly in the most economical temperature control mode. Compared to the system's performance before the algorithm's implementation, the control is optimized, resulting in a 19.4% energy saving.