This paper studies the problem of thermal control of buildings from the perspective of partitioning them into clusters for decentralized control. A measure of deviation in performance between centralized and decentralized control in the model predictive control framework, referred to as the optimality loss factor, is derived. Another quantity called the fault propagation metric is introduced as an indicator of the robustness of any decentralized architecture to sensing or communication faults. A computationally tractable agglomerative clustering approach is then proposed to determine the decentralized control architectures, which provide a satisfactory trade-off between the underlying optimality and robustness objectives. The potential use of the proposed partitioning methodology is demonstrated using simulated examples.
- Decentralized control
- Model predictive control
- Optimal control
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering