Application of linear multi-agent system approach to design building temperature control system

Abstract

Building temperature control system (BTCS) is a large-scale interconnected system with a high demand of energy consumption in building. BTCS is generally described by linear time-invariant system using electric-analogous devices such as resistors and capacitors. In view of graph theory, it is modeled as linear multi-agent system (MAS) subjected to undirected communication topology. The main goal of BTCS is to track the reference temperature trajectory which is equivalent to make the tracking error go to zero in finite time. Hence, BTCS properly suits with framework of distributed control, decentralized control, and centralized control. In this thesis, we focus on comparison of three design methods, namely, distributed consensus controllers (DCC), decentralized iterative learning control (ILC), and centralized ILC. First, the DCC is applied to BTCS by solving resource allocation problem with hard control input constraints. Next, we apply decentralized ILC using the derivative of tracking error (D-type). Lastly, centralized ILC design is formulated as minimization problem with a quadratic cost function subject to control input constraints. This problem is also called Q-ILC. We apply the Alternating Direction Method of Multipliers (ADMM) approach to Q-ILC design and derive analytical solution of control input in each iteration. The convergence property of ADMM and Q-ILC algorithm are given. Numerical results are provided to illustrate the effectiveness of these algorithms.