Decentralized state estimation for different substructure layouts of an adaptive high-rise structure

Active control in adaptive structures allows for reducing the structural weight of a building and thus to drastically decrease the construction sector’s global impact. For an active control, it is necessary to estimate the state. Especially for large structures, decentralized approaches are particularly advantageous. However, the choice of the individual decentralized models, the substructuring, is important. This work considers decentralized state estimation for different substructure layouts obtained by the Relative Gain Array (RGA) for an adaptive high-rise structure. The estimators are realized using Information Filters (IF) based on reduced models derived from the full model via SEREP-Guyan and modal reduction. Different degrees of interconnection (none, sparse, full) are investigated w. r. t. estimation accuracy and robustness towards communication failure. For more substructures the estimation error increases. Depending on the layout, communicating is beneficial, but a full interconnection is not necessary for a sufficient estimation. In case of a failed information exchange, communicating estimators should adapt to the fault.