  We study the problem of performance optimization of closed-loop control
systems with unmodeled dynamics. Bayesian optimization (BO) has been
demonstrated to be effective for improving closed-loop performance by
automatically tuning controller gains or reference setpoints in a model-free
manner. However, BO methods have rarely been tested on dynamical systems with
unmodeled constraints and time-varying ambient conditions. In this paper, we
propose a violation-aware contextual BO algorithm (VACBO) that optimizes
closed-loop performance while simultaneously learning constraint-feasible
solutions under time-varying ambient conditions. Unlike classical constrained
BO methods which allow unlimited constraint violations, or 'safe' BO algorithms
that are conservative and try to operate with near-zero violations, we allow
budgeted constraint violations to improve constraint learning and accelerate
optimization. We demonstrate the effectiveness of our proposed VACBO method for
energy minimization of industrial vapor compression systems under time-varying
ambient temperature and humidity.
