  Accelerator performance often deteriorates with time during a long period of
operation due to secular changes in the machine components or the surrounding
environment. In many cases some tuning knobs are effective in compensating the
performance drifts and optimization methods can be used to find the ideal
machine setting. However, such intervention usually cannot be done without
interrupting user operation as the optimization algorithms can substantially
impact the machine performance. We propose an optimization algorithm, Safe
Robust Conjugate Direction Search (RCDS-S), which can perform accelerator
tuning while keeping the machine performance within a designated safe envelope.
The algorithm builds probability models of the objective function using
Lipschitz continuity of the function as well as characteristics of the drifts
and applies to the selection of trial solutions to ensure the machine operates
safely during tuning. The algorithm can run during normal user operation
constantly, or periodically, to compensate the performance drifts. Simulation
and online tests have been done to validate the performance of the algorithm.
