Clean-Room Air Conditioning Control Based on Improved Min-Max Robust Model Predictive Control Algorithm

Abstract

With the increasing demand for indoor environmental comfort, air conditioning has become an essential electrical device for modern life. To improve the comfort of indoor environment in clean-room and to reduce the operating cost of the equipment, the study proposes an improved Min-Max robust model predictive control algorithm for clean-room air conditioning control strategy. Firstly, for the comfort of indoor air conditioning, the MPC algorithm is investigated to regulate the air conditioning air supply based on indoor temperature and humidity ratio in order to control the indoor comfort of the clean-room. Subsequently, an improved Min Max robust model predictive control algorithm was proposed to control the parameters of the model, and compared and analyzed with the original algorithm. The simulation results indicated that the PMV index of the model predictive control algorithm was 0.66 at the lowest and 0.68 at the highest. The Min-Max robust model predictive control algorithm was 0.73 at the lowest and 4.3 at the highest. The improved Min-Max robust model predictive control algorithm was 0.45 at the lowest and 0.47 at the highest. The experimental data indicate that the improved Min-Max robust model predictive control algorithm has the best performance and has more obvious effect on the comfort control of clean room environment.

Keywords

• SMPC
• environmental comfort
• operating costs
• air conditioning control
• heat and humidity ratio effects