The low structural damping of large space structures and the stringent positioning requirements in missions demand effective vibration suppression. The semi-active approach at hand is based on friction damping due to interfacial slip in semi-active joints which can be controlled by varying the normal pressure in the contact area using a piezo-stack actuator. This paper focuses on the modeling, identification and model reduction of a large space structure with semi-active joints. For the purpose of model identification and model reduction, the nonlinear friction forces transmitted in the joints are considered as external forces acting on the linear tress structure. Experimental Modal Analysis results are used to update the FE model of the truss structure and the parameters of the nonlinear friction model are identified from measured responses of an isolated joint. The model of the linear subsystem is reduced by a combination of balanced reduction and matching moments method. The modal truncation is based on controllability and observability gramians. To improve the fidelity locations conventional connections are replaced by adaptive joints, each with a local feedback controller for the adaptation of the normal force. Simulation results of a 10-bay truss structure with semi-active joints show the potential of the present approach.
The low structural damping of large space structures and the stringent positioning requirements in missions demand effective vibration suppression. The semi-active approach at hand is based on friction damping due to interfacial slip in semi-active joints which can be controlled by varying the normal pressure in the contact area using a piezo-disc actuator. This paper focuses on the optimal placement of semi-active joints for vibration suppression. The proposed method uses optimality criteria for actuator and sensor locations based on eigenvalues of the controllability and observability gramians. It is stated as a nonlinear multicriteria optimization problem with discrete variables which is solved by a stochastic search algorithm. As final step in the design procedure, parameters of the local feedback controllers assigned to each adaptive joint are optimized with respect to transient response of the structure. The present method is applied to a 10-bay truss structure. Simulation runs of the controlled structure are used to verify the optimization results.
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