A monte carlo approach to the analysis of control system robustness

Laura Ryan Ray, Robert F. Stengel

Research output: Contribution to journalArticlepeer-review

190 Scopus citations


Stochastic robustness, a simple technique used to estimate the stability and performance robustness of linear, time-invariant systems, is described. The scalar probability of instability is introduced as a measure of stability robustness. Examples are given of stochastic performance robustness measures based on classical time-domain specifications. The relationship between stochastic robustness measures and control system design parameters is discussed. The technique is demonstrated by analysing an LQG/LTR system designed for a flexible robot arm. It is concluded that the analysis of stochastic robustness offers a good alternative to existing robustness metrics. It has direct bearing on engineering objectives, and it is appropriate for evaluating robust control system synthesis methods currently practised.

Original languageEnglish (US)
Pages (from-to)229-236
Number of pages8
Issue number1
StatePublished - Jan 1993

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


  • Monte Carlo methods
  • Robustness
  • control system analysis
  • multivariable control systems


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