Distributed MPC-ILC Thermal Control Design for Large-Scale Multi-Zone Building HVAC System

Abstract

With building heating, ventilation, and air conditioning (HVAC) systems accounting for 50% the energy consumption in the building sector in the United States, there is a need to develop and implement optimal control strategies for building HVAC systems that reduce energy consumption while achieving thermal comfort for users. In the author's previous work, an integrated model predictive control (MPC) and iterative learning control (ILC) design approach was presented that took advantage of both controllers. It did not rely on model accuracy compared to conventional MPC and reduced the learning curve compared to conventional ILC. Albeit the previous numerical results showed fast convergence in most of the VAV subsystems, the gap between room air temperature and its set point remains noticeable in several zones. This paper further proposes an approach to extend the centralized MPC-ILC controller to take into account the distributed factor and the spatial distribution of the thermal zones of the VAV system. The improved control strategy allows all VAVs to interact with each other and contribute collectively to the overall convergence of the whole system. The proposed controller is implemented on a thirty-two zone VAV reheat system and compared with different controllers, including our previous MPC-ILC design. The outcomes show that the proposed controller results in faster and closer convergence among all zones even when the number of subsystems is large.