Adaptive deadband control of a drifting process with unknown parameters

Adjusting a drifting process to minimize the expected sum of quadratic off-target and fixed adjustment costs is considered under unknown process parameters. A Bayesian approach based on sequential Monte Carlo methods is presented. The benefits of the resulting “deadband” adjustment policy are studied.