Monte Carlo Analysis

Sample uncertain systems for statistical analysis

You can perform Monte Carlo Analysis by analyzing the behavior of random samples taken from an uncertain system. For instance, use usample to obtain an array of numeric models from an uncertain model by sampling the uncertain control design blocks.

Functions

`usample (LTI models)`	Generate random samples of uncertain model or element
`usample (Simulink)`	Generate random samples of uncertain variables in a Simulink model
`rsampleBlock`	Randomly sample Control Design blocks in generalized model
`usubs`	Substitute given values for uncertain elements of uncertain objects
`gridureal`	Grid `ureal` parameters uniformly over their range
`complexify`	Replace `ureal` atoms by summations of `ureal` and `ucomplex` (or `ultidyn`) atoms

Topics

Sample Uncertain Systems
Generate random samples of uncertain systems from within the modeled uncertainty range.
Generate Samples of Uncertain Systems
Use the usample function to randomly sample an uncertain model, returning non-uncertain instances of the uncertain model.
Evaluate Uncertain Elements by Substitution
Evaluate uncertain elements at particular values of their uncertain parameters, or sample them at multiple parameter values.
Substitution by usubs
Use the usubs command to set uncertain elements of an uncertain model to fixed values.
Model Arrays
Store multiple dynamic system objects in a single MATLAB^® array for multiple-model design and analysis.
Array Management for Uncertain Objects
Arrays of uncertain models behave similarly to arrays of fixed-coefficient LTI models.