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【摘要】This paper investigates the problem of robust L1 model reduction for continuous-time uneertain stochastie time-delay systems. For a given mean-square stable system, our purpose is to construct reduced-order systems, such that the error system between these two models is mean-square asymptotically stable and has a guaranteed L1(also called peak-to-peak) performance. The peak-to-peak gain criterion is first established for stochastic time-delay systems, and the corresponding model reduction problem is solved by using projection lemma. Sufficient eonditions are obtained for the existence of admissible reduced-order models in terms of linear matrix inequalities (LMIs) phls matrix inverse eonstraints. Since these obtained conditions are not expressed as strict LMls, the cone complementarity linearization (CCL) method is exploited to cast them into nonlinear minimization problems subject to LMI constraints, which can be readily solved by standard numerical software. In addition, the development of reduced-order models with special structures, such as the delay-free model, is also presented. The efficiency of the proposed methods is demonstrated via a numerieal example.