A model-based predictive supervisory controller for multi-evaporator HVAC systems

Multi-evaporator vapor compression cooling systems are representative of the complex, distributed nature of modern HVAC systems. Earlier research efforts focused on the development of a decentralized control architecture for individual evaporators that exploits the constraint-handling capabilities of model predictive control while regulating the pressure and cooling setpoints. This paper presents a global controller that generates the setpoints for the local controllers; this controller balances the goals of cooling zone temperature tracking with optimal energy consumption. To accommodate the inherent limitations of the system, a model predictive control (MPC) based approach is used. The improved efficiency and the effects of the tuning parameters are demonstrated upon an experimental system.