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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.

Functions

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robstabRobust stability of uncertain system
robgainRobust performance of uncertain system
uscaleScale uncertainty of block or system (Since R2020a)
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

Topics

Robustness Analysis