  Energy requirements for heating and cooling of buildings constitute a major
fraction of end use energy consumed. Therefore, it is important to provide the
occupant comfort requirements in buildings in an energy efficient manner.
However, buildings are large scale complex systems, susceptible to sensor,
actuator or communication network failures in their thermal control
infrastructure, that can affect their performance in terms of occupant comfort
and energy efficiency. The degree of decentralization in the control
architecture determines a fundamental tradeoff between performance and
robustness. This thesis studies the problem of thermal control of buildings
from the perspective of partitioning them into clusters for decentralized
control, to balance underlying performance and robustness requirements.
Measures of deviation in performance and robustness between centralized and
decentralized architectures in the Model Predictive Control framework are
derived. Appropriate clustering algorithms are then proposed to determine
decentralized control architectures which provide a satisfactory trade-off
between the underlying performance and robustness objectives. Two different
partitioning methodologies the CLF-MCS method and the OLF-FPM method are
developed and compared. The problem of decentralized control design based on
the architectures obtained using these methodologies is also considered. It
entails the use of decentralized extended state observers to address the issue
of unavailability of unknown states and disturbances in the system. The
potential use of the proposed control architecture selection and decentralized
control design methodologies is demonstrated in simulation on a real world
multi-zone building.
