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          发布人:刘玉菡  发布时间:2019-12-30   浏览次数:10


          时间:202013日(周五) 上午9:30


          报告题目: Probabilistic robust control and estimation: a polynomial chaos approach


          Numerous studies have been reported on robust control and estimation subject to model uncertainties. The widely used worst-case strategy tends to produce highly conservative performance because the worst-case scenario may have vanishingly low probability of occurrence. In contrast to a worst-case performance bound, practical interest in the performance variation or dispersion across the uncertainty region has motivated recent research on probabilistic robustness. For general nonlinear uncertainty structure, the most widely recognized approach is various randomized algorithms using Monte-Carlo sampling. However, the convergence of its solution with respect to the number of samples is generally slow, thereby it often results in intensive computation with a large sample size. In this talk, we present a computationally efficient nonsampling alternative by exploiting polynomial chaos theory. Both control and state estimation problems will be discussed for linear systems with nonlinear dependence on probabilistic parametric uncertainties.