The performance of advanced controllers depends on the selection of
several tuning parameters that can affect the closed-loop control
performance and constraint satisfaction in highly nonlinear and
nonconvex ways. There has been a significant interest in auto-tuning of
complex control structures using Bayesian optimization (BO). However, an
open challenge is how to deal with uncertainties in the closed-loop
system that cannot be attributed to a lumped, small-scale noise term.
This paper develops an adversarially robust BO (ARBO) method that is
suited to auto-tuning problems with significant time-invariant
uncertainties in a plant simulator. ARBO uses a Gaussian process model
that jointly describes the effect of the tuning parameters and
uncertainties on the closed-loop performance. ARBO uses an alternating
confidence-bound procedure to simultaneously select the next candidate
tuning and uncertainty realizations, implying only one expensive
closed-loop simulation is needed at each iteration. The advantages of
ARBO are demonstrated on two case studies.
Adversarially Robust Bayesian Optimization for Efficient
Auto‐Tuning
of Generic Control Structures under Uncertainty
