Model predictive control of indoor thermal environment conditioned by a direct expansion air conditioning system

Abstract

Temperature and humidity are two important factors that influence both indoor thermal comfort and air quality. Through varying compressor and supply fan speeds of a direct expansion (DX) air conditioning (A/C) unit, the air temperature and humidity in the conditioned space can be regulated simultaneously. However, most existing controllers are designed to minimize the tracking errors between the system outputs with their corresponding settings as quickly as possible. The energy consumption, which is directly influenced by the compressor and supply fan speeds, is not considered in the relevant controller formulations, and thus the system may not operate with the highest possible energy efficiency. To effectively control temperature and humidity while minimizing the system energy consumption, a model predictive control (MPC) strategy was developed for a DX A/C system, and the development results are presented in this paper. A physically-based dynamic model for the DX A/C system with both sensible and latent heat transfers being considered was established and validated by experiments. To facilitate the design of MPC, the physical model was further linearized. The MPC scheme was then developed by formulating the objective function which sought to minimize the tracking errors of temperature and moisture content while saving energy consumption. Based on the results of command following and disturbance rejection tests, the proposed MPC scheme was capable of controlling temperature and humidity with adequate control accuracy and sensitivity. In comparison to linear-quadratic-Gaussian (LQG) controller, better control accuracy and lower energy consumption could be realized when using the proposed MPC strategy to simultaneously control temperature and humidity.