This paper addresses the issues of conservativeness and computational complexity of probabilistie robustness analysis. The authors solve both issues by defining a new sampling strategy and robustness measure. The new ...This paper addresses the issues of conservativeness and computational complexity of probabilistie robustness analysis. The authors 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, the authors 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.展开更多
基金supported in part by grants from NASA (NCC5-573)LEQSF (NASA /LEQSF(2001-04)-01)+1 种基金the NNSFC Young Investigator Award for Overseas Collaborative Research (60328304)a NNSFC grant (10377004)
文摘This paper addresses the issues of conservativeness and computational complexity of probabilistie robustness analysis. The authors 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, the authors 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.
