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M icrovibrations, generally de ned as low-amplitude vibrations at frequencies up to 1 kH z, are of critical importance in a number of areas. It is now well known that, in general, the suppression of such microvibrations to acceptable levels requires the use of active control techniques which, in turn, require suf ciently accurate and tractable models of the underlying dynamics on which to base controller design and initial performance evaluation. Previous work has developed a systematic procedure for obtaining a nite-dimensional state-space model approximation of the underlying dynamics from the de ning equations of motion, which has then been shown to be a suitable basis for robust controller design. In this paper, the experimental validation of this model prior to experimental studies is described in order to determine the effectiveness of the designed controllers. This includes details of the experimental rig and also the use of methods for assessing the safety of the resulting structure against uncertain parameters.

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Microvibrations, at frequencies between 1 and 1000 Hz, generated by on board equipment, can propagate throughout a spacecraft structure and affect the performance of sensitive payloads. To investigate strategies to reduce these dynamic disturbances by means of active control systems, realistic yet simple structural models are necessary to represent the dynamics of the electromechanical system. In this paper a modeling technique which meets this requirement is presented, and the resulting mathematical model is used to develop some initial results on active control strategies. Attention is focused on a mass loaded panel subjected to point excitation sources, the objective being to minimize the displacement at an arbitrary output location. Piezoelectric patches acting as sensors and actuators are employed. The equations of motion are derived by using Lagrange's equation with vibration mode shapes as the Ritz functions. The number of sensors/actuators and their location is variable. The set of equations obtained is then transformed into state variables and some initial controller design studies are undertaken. These are based on standard linear systems optimal control theory where the resulting controller is implemented by a state observer. It is demonstrated that the proposed modeling technique is a feasible realistic basis for in-depth controller design/evaluation studies.

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