Distributed iterative learning temperature control for multi-zone HVAC system

Abstract

By taking the dynamic features of the multi-zone heating ventilation air conditioning (HVAC) system into consideration, a distributed iterative learning temperature control (DILTC) method is proposed for building rooms where the exact values of the thermal capacitances, as the key parameters of the HVAC system, are unavailable. At first, an iterative dynamic linearization is applied to reformulate the HVAC system into an all-purposed linear incremental form virtually to facilitate the controller design without relying on model information. On this basis, a data-driven DILTC method is proposed with rigorous convergence analysis where the topology among rooms is considered via a thermal resistance matrix. Under the developed convergence conditions, the tracking error is guaranteed to be iteratively decreased to a small bound with nonrepetitive disturbances. If the disturbances are completely repeatable, a perfect tracking performance can be achieved. The results have also been extended to the HVAC systems subjected to I/O constraints to address the limited heating and cooling capacities. Through extensive simulations, we confirm the good efficiency and applicability of the proposed methods.