A controller-performance-assessment algorithm is developed to analyze the closed-loop behavior and modify the parameters of a control system employed in automated insulin delivery. To this end, various performance indices are defined to quantitatively evaluate the controller efficacy in real-time. The controller assessment and modification module also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects. A dynamic safety constraint derived from estimates of the physiological states ensures safety of the controlled drug dosing. Using a multivariable simulation platform for type 1 diabetes mellitus, the controller assessment and modification module is applied to the problem of regulating glucose concentrations in people with diabetes by means of automated insulin delivery with an artificial pancreas, and the results demonstrate the improvement in controller performance using the performance-assessment module.