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Robustness and Worst-Case Analysis

Worst-case effect of uncertainty on stability, margins, and overshoot

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 while maintaining stability or desired performance.


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robstabRobust stability of uncertain system
robgainRobust performance of uncertain system
uscaleScale uncertainty of block or system
robOptionsOption set for robustness analysis
gapmetricGap metric and Vinnicombe (nu-gap) metric for distance between two systems
lncfLeft normalized coprime factorization
rncfRight normalized coprime factorization
ncfmarginCalculate normalized coprime stability margin of plant-controller feedback loop
loopsensSensitivity functions of plant-controller feedback loop
sdhinfnormCompute L2 norm of continuous-time system in feedback with discrete-time system
sdlsimTime response of sampled-data feedback system
wcgainWorst-case gain of uncertain system
wcsigmaplotPlot worst-case gain of uncertain system
wcnormWorst-case norm of uncertain matrix
wcOptionsOption set for worst-case analysis
mussvCompute bounds on structured singular value (µ)
mussvextractExtract muinfo structure returned by mussv


Robustness Analysis