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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Stengel, Robert F. Ryan, Laura E. |
| Copyright Year | 1989 |
| Description | Author affiliation: Graduate student, Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, N.J. 08544 (Ryan, Laura E.) || Professor, Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, N.J. 08544 (Stengel, Robert F.) |
| Abstract | A simple numerical procedure for estimating the stochastic robustness of a linear, time-invariant system is described. Based on Monte Carlo evaluation of the system's eigenvalues, this analysis approach introduces the probability of instability as a scalar measure of stability robustness. The related stochastic root locus, a portrayal of the root probability density, provides insight into robustness characteristics. Parameter uncertainties are not limited to Gaussian distributions; non-Gaussian cases, including uncertain-but-bounded variations, can be considered as well. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. An example demonstrates stochastic robustness as applied to a physical system with Gaussian, uniformly distributed, and binary parameters. |
| Starting Page | 937 |
| Ending Page | 945 |
| File Size | 1011771 |
| Page Count | 9 |
| File Format | |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1989-06-21 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Histograms Control system analysis Control systems Robust control Robustness Stochastic systems Robust stability Monte Carlo methods Eigenvalues and eigenfunctions Stability analysis |
| Content Type | Text |
| Resource Type | Article |
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