  An autonomous adaptive MPC architecture is presented for control of heating,
ventilation and air condition (HVAC) systems to maintain indoor temperature
while reducing energy use. Although equipment use and occupant changes with
time, existing MPC methods are not capable of automatically relearning models
and computing control decisions reliably for extended periods without
intervention from a human expert. We seek to address this weakness. Two major
features are embedded in the proposed architecture to enable autonomy: (i) a
system identification algorithm from our prior work that periodically re-learns
building dynamics and unmeasured internal heat loads from data without
requiring re-tuning by experts. The estimated model is guaranteed to be stable
and has desirable physical properties irrespective of the data; (ii) an MPC
planner with a convex approximation of the original nonconvex problem. The
planner uses a descent and convergent method, with the underlying optimization
problem being feasible and convex. A year long simulation with a realistic
plant shows that both of the features of the proposed architecture - periodic
model and disturbance update and convexification of the planning problem - are
essential to get the performance improvement over a commonly used baseline
controller. Without these features, though MPC can outperform the baseline
controller in certain situations, the benefits may not be substantial enough to
warrant the investment in MPC.
