Abstract For complex mechatronic systems it is convenient to modularize the system into a hierarchical structure. Especially for self-optimizing systems hierarchies can be used to reduce the complexity. Such systems h...
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Abstract For complex mechatronic systems it is convenient to modularize the system into a hierarchical structure. Especially for self-optimizing systems hierarchies can be used to reduce the complexity. Such systems have an extensive information processing, because they adapt their behavior to varying system and environment conditions in an autonomous way. In this paper we present a parametric model-order reduction based on multi-moment matching that is used to simplify hierarchical models. By this procedure the execution of a hierarchical multiobjective optimization is fastened. We compare our hierarchical approach with a multiobjectiveoptimization of a non-reduced model for an active suspension system. A good approximation of both the Pareto sets and Pareto fronts is obtained by our approach.
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