A statistical approach for performance analysis of uncertain systems

Abstract

This paper addresses the issues of conservativeness and computational complexity of probabilistic robustness analysis. We solve both issues by defining a new sampling strategy and robustness measure. The new measure is shown to be much less conservative than the existing one. The new sampling strategy enables the definition of efficient hierarchical sample reuse algorithms that reduce significantly the computational complexity and make it independent of the dimension of the uncertainty space. Moreover, we show that there exists a one to one correspondence between the new and the existing robustness measures and provide a computationally simple algorithm to derive one from the other.

Uncertainty inference with applications to control systems

“…Theorem 8 is established in [6,7]. A proof is reproduced in Appendix C. More generally, we have the following results.…”

Asymptotically Optimal Sequential Estimation of the Mean Based on Inclusion Principle

“…Section 5 is the conclusion. The statistical methodology proposed in this paper has been applied to electrical engineering, computer science and medical sciences, see recent literature [2,3,4,5,6,7,8,12] and the references therein.…”