To be robust, your control system should meet your stability and performance requirements for all possible values of uncertain parameters. Monte Carlo parameter sampling via usample can be used for this purpose as shown in System with Uncertain Parameters, but Monte Carlo methods are inherently hit or miss. With Monte Carlo methods, you might need to take an impossibly large number of samples before you hit upon or near a worst-case parameter combination.
Robust Control Toolbox™ software gives you a powerful assortment of robustness analysis commands that let you directly calculate upper and lower bounds on worst-case performance without random sampling.
Worst-Case Robustness Analysis Commands
Comprehensive analysis of feedback loop
Sensitivity functions of feedback loop
Normalized coprime stability margin of feedback loop
Robust performance of uncertain systems
Stability margins of uncertain systems
Worst-case gain of an uncertain system
Worst-case gain/phase margins for feedback loop
Worst-case sensitivity functions of feedback loop