### Abstract

A recently developed new paradigm for probabilistic robustness analysis does not require apriori information about the underlying distribution function for the uncertain parameters; only a mild monotonicity and symmetry assumption is involved. The starting point is exactly the same as in classical robustness theory - a system with uncertain parameters which are only known within given bounds. However, instead of calculating the classical robustness margin for such a system, a risk-adjusted margin is sought. The theory suggests that the "best" way to sample the uncertain parameters is not necessarily the most intuitive way. That is, the sampling distribution to use is not something obvious such as a normal or uniform distribution. The main objective of this paper is to demonstrate that these "counterintuitive" predictions of the theory are not just mathematical possibilities but actually admit physical realizations.

Original language | English (US) |
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Title of host publication | Proceedings of the 1998 American Control Conference, ACC 1998 |

Pages | 1427-1428 |

Number of pages | 2 |

DOIs | |

State | Published - Dec 1 1998 |

Event | 1998 American Control Conference, ACC 1998 - Philadelphia, PA, United States Duration: Jun 24 1998 → Jun 26 1998 |

### Publication series

Name | Proceedings of the American Control Conference |
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Volume | 3 |

ISSN (Print) | 0743-1619 |

### Other

Other | 1998 American Control Conference, ACC 1998 |
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Country | United States |

City | Philadelphia, PA |

Period | 6/24/98 → 6/26/98 |

### All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering

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## Cite this

*Proceedings of the 1998 American Control Conference, ACC 1998*(pp. 1427-1428). [707060] (Proceedings of the American Control Conference; Vol. 3). https://doi.org/10.1109/ACC.1998.707060