Robust Control Toolbox

Performing Robustness Analysis

Using 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 its entire operating range, and what source of uncertainty is most likely to jeopardize performance.

Robustness of Servo Controller for DC Motor
Model uncertainty in DC motor parameters and analyze the effect of this uncertainty on motor controller performance.

You can randomize the model uncertainty to perform Monte Carlo analysis. Alternatively, you can use more direct tools based on mu-analysis and linear matrix inequality (LMI) optimization; these tools identify worst-case scenarios without exhaustive simulation.

Robust Control Toolbox provides functions to assess worst-case values for:

• Gain and phase margins, one loop at a time
• Stability margins that take loop interactions into account
• Gain between any two points in a closed-loop system
• Sensitivity to external disturbances

These functions also provide sensitivity information to help you identify the uncertain elements that contribute most to performance degradation. With this information, you can determine whether a more accurate model, tighter manufacturing tolerances, or a more accurate sensor would most improve control system robustness.

Nominal and worst-case rejection of a step disturbance (top) and Bode diagram of a sensitivity function (bottom). Robust Control Toolbox lets you analyze the effect of plant model uncertainty on closed-loop stability and control system performance.