Iterative learning control in large scale HVAC system

Abstract

Heating, ventilating and air-conditioning (HVAC) system is a multi-variable, strongly coupled, nonlinear, time variant, large time delay and large-scale system composed of several subsystems. In order to save energy, all the subsystems should work coordinately in different working points to meet the people's comfortable requirement. In HVAC control systems, system optimal control inputs or optimal operating points, which can be acquired through supervisory and optimal control, can ensure minimum energy cost and satisfy indoor comfort and air quality, taking into account the ever-changing indoor and outdoor conditions as well as the characteristics of HVAC systems. A variable air volume (VAV) variable water volume (VWV) air-conditioning system is wholly analyzed with large-scale system theory based on “decomposition and coordination” strategy. Iterative learning control (ILC) strategy is introduced first into a large-scale HVAC system, and the effectiveness of the ILC strategy is demonstrated through a case study. Results show that as the number of iteration increases, the system tracking error over the entire operation will decrease and eventually vanish. This means that the good performance of subsystems can be maintained under the ILC strategy when the working points change with the dynamic load.