Semi-automated optimal vibration suppression of flexible structures

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The overall objective of the proposed research program is to develop an innovative approach for optimum vibration suppression of flexible structures. Commonly used passive vibration absorbers become de-tuned over time due to the structural variations. In order to compensate for these deviations, active vibration control schemes are needed. These techniques, however, suffer from the control induced instability in addition to the large control effort requirement. Utilizing concurrent use of semi-active and active subsections, an intelligent semi-automated alternative is suggested. The semi-active subsection includes smart structures ranging from ER/MR dampers to magnetostrictive actuators, and a particular set of piezoelectric actuators forms the active subsection. By altering the adjustable structural properties (in semi-active unit) and control parameters (in active unit), a search is conducted to minimize an objective function subject to constraints, which may reflect performance characteristics. In practice, an engineer in-the-loop interprets the resulting performance and may initiate a number of options: control optimization, semi-active component optimization, or combination of the two. The predominant activity of the proposed study will be to develop and facilitate this interpretive decision making via numerical tools. The vibration should be suppressed to the possible extent using a semi-active unit (the least power consuming effort) through proper adjustment of smart materials, while the active absorber unit handles the remaining vibration suppression requirement. If the resulting design is not acceptable or if system parameters show variations, an interpretation procedure triggers to alter the parameters of the semi-active unit, or retune the control parameters of active unit, or concurrently adjust both. These features are very important and will broaden the utility of the vibration absorbers in industrial applications where a wide range of structural variations is encountered. The feasibility of the proposed method is demonstrated through the simulations of a SDOF system. The challenge lies in the development of intelligent decision-making processes for hybrid treatment when implemented on realistic systems with complex dynamics and controllability.