LoLiMoT based MPC for air handling units in HVAC systems

• Simultaneous control of air temperature and relative air humidity.
• MPC strategy based on a network of local linear models is proposed.
• Local linear models are derived from a detailed physical plant model using LoLiMoT.
• The performance of the concept is demonstrated in ​simulation and by real world tests.
• The proposed concept is compared to a classical PI strategy.

This paper deals with the multi-input-multi-output (MIMO) control of an air handling unit (AHU) in an industrial heating ventilating and air conditioning (HVAC) system. The temperature and the relative humidity of the supply air have to be controlled simultaneously. Due to the nonlinear behavior of such a AHU it is difficult to achieve satisfactory performance over the whole operating range using linear control. Therefore in this work a model predictive control (MPC) strategy based on a network of local linear models is proposed. A detailed mathematical model referred to as “physical model” of the AHU is derived from physical laws and is verified by measurements. By proper excitation of the physical model, the local linear models are determined using the so called LoLiMoT (Local Linear Model Tree)-algorithm. The proposed concept is compared to a conventional PI control strategy and its performance is demonstrated in simulation as well as on a real world test plant.