Fast universal algorithms for robustness analysis

• Published in: 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475) Vol. 2; pp. 1926 - 1931 Vol.2
• Main Authors: Xinjia Chen, Kemin Zhou, Aravena, J.L.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Algorithm design and analysis; Computational complexity; Control systems; Degradation; Heart; NASA; Random variables; Robustness; State estimation; Uncertainty
• ISBN: 9780780379244; 0780379241
• ISSN: 0191-2216
• DOI: 10.1109/CDC.2003.1272897

In this paper, we develop efficient randomized algorithms for estimating probabilistic robustness margin and constructing robustness degradation curve for uncertain dynamic systems. One remarkable feature of these algorithms is their universal applicability to robustness analysis problems with arbitrary robustness requirements and uncertainty bounding set. In contrast to existing probabilistic methods, our approach does not depend on the feasibility of computing deterministic robustness margin. We have developed efficient methods such as probabilistic comparison, probabilistic bisection and backward iteration to facilitate the computation. In particular, confidence interval for binomial random variables has been frequently used in the estimation of probabilistic robustness margin and in the accuracy evaluation of estimating robustness degradation function. Motivated by the importance of fast computation of binomial confidence interval in the context of probabilistic robustness analysis, we have derived an explicit formula for constructing the confidence interval of binomial parameter with guaranteed coverage probability. The formula overcomes the limitation of normal approximation which is asymptotic in nature and thus inevitably introduce unknown errors in applications. Moreover, the formula is extremely simple and very tight in comparison with classic Clopper-Pearson's approach.