  We study the problem of performance optimization of closed-loop control
systems with unmodeled dynamics. Bayesian optimization (BO) has been
demonstrated 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. In this paper, we propose a violation-aware BO algorithm (VABO)
that optimizes closed-loop performance while simultaneously learning
constraint-feasible solutions. Unlike classical constrained BO methods which
allow an 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 VABO method for
energy minimization of industrial vapor compression systems.
