Distributed iterative learning temperature control for large-scale buildings

This article investigates the distributed iterative learning control for heating ventilation and air condition (HVAC) systems. Large-scale building HVAC systems consisting of several subsystems are generally disturbed by the external environment and random human activities. The control objective is to achieve all units of the large-scale building consistently maintaining the desired temperature. A distributed learning control (DLC) scheme with a decreasing gain is proposed and analyzed for the first time. To accelerate convergence speed, an adaptation mechanism is introduced to generate an adaptive learning gain sequence. Then, an accelerated distributed learning control (ADLC) scheme is designed to improve the transient convergence performance. For the DLC scheme, the input error is proved convergent to zero in the mean-square sense, whereas the convergence with probability 1 is proved for the ADLC scheme. Numerical results are presented to verify the effectiveness of the proposed schemes.