  Tuning machine parameters of particle accelerators is a repetitive and
time-consuming task that is challenging to automate. While many off-the-shelf
optimization algorithms are available, in practice their use is limited because
most methods do not account for safety-critical constraints in each iteration,
such as loss signals or step-size limitations. One notable exception is safe
Bayesian optimization, which is a data-driven tuning approach for global
optimization with noisy feedback. We propose and evaluate a step-size limited
variant of safe Bayesian optimization on two research facilities of the Paul
Scherrer Institut (PSI): a) the Swiss Free Electron Laser (SwissFEL) and b) the
High-Intensity Proton Accelerator (HIPA). We report promising experimental
results on both machines, tuning up to 16 parameters subject to 224
constraints.
