A robust control system meets stability and performance requirements for all possible values of uncertain parameters. Although Monte-Carlo parameter sampling can yield a general idea of system performance across all uncertainty ranges, it cannot produce a guaranteed analysis of the worst-case parameter combination. The robustness analysis commands in this category directly calculate the upper and lower bounds on worst-case performance without random sampling. You can also calculate robustness margins that tell you how much variation in uncertain parameters the system can tolerate.
||Worst-case gain of uncertain system|
||Plot worst-case gain of uncertain system|
||Worst-case norm of uncertain matrix|
||Calculate worst-case sensitivity and complementary sensitivity functions of plant-controller feedback loop|
||Worst-case disk stability margins of uncertain feedback loops|
||Option set for worst-case analysis|
Understand the relationships among measures of robust stability, robust performance, and worst-case gain.
Calculate the robust stability and examine the worst-case gain of a closed-loop uncertain system.
Analyze and quantify the robustness of feedback control systems with uncertainty, and understand the relationship between robustness and the structured singular value, μ.
Create a MIMO system with parametric uncertainty and analyze it for robust stability and worst-case performance.
Systems with only real uncertain parameters can have discontinuities in the structured singular value μ that hide robustness issues.