Robust Control Toolbox 3.4.1

Performing Robustness Analysis

Using the Robust Control Toolbox, you can analyze the effect of plant model uncertainty on the closed-loop stability and performance of the control system. In particular, you can determine whether your control system will perform adequately over your entire operating range, and what source of uncertainty is most likely to jeopardize performance.

You can randomize the model uncertainty to perform Monte-Carlo analysis. Alternatively, you can use more direct tools based on µ-analysis and LMI optimization. These tools use sophisticated algorithms to identify worst-case scenarios without exhaustive simulation.

The Robust Control Toolbox provides tools to assess:

• Worst-case gain/phase margins one loop at a time
• Worst-case stability margins taking loop interactions into account
• Worst-case gain between any two points in the closed-loop system
• Worst-case sensitivity to external disturbances

These tools also provide sensitivity information to help you identify which uncertain elements contribute most to performance degradation. You can then determine whether a more accurate model, tighter manufacturing tolerances, or a better sensor would most improve control system robustness.