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Recently, an increasingly insatiable demand in the international marketplaces for higher-performance in structural and mechanical systems at various fields such as aerospace, defense, automotive and manufacturing industries has evolved ultra-advanced structures, so called smart structures [1]. The smart structures have their inherent adaptive capabilities, which are of multi-disciplinary areas pertaining to design, fabrication, sensing, actuating, and microprocessor-based real-time control. As one of principal ingredients of the smart structures, the actuating technology is typically exploited to dramatically tune global mechanical properties of the structures, or else to dynamically tailor the shapes of the structures in an orchestrated manner. As of now, electro-rheological fluids, piezoelectric materials (film or ceramic), and shape memory alloys have been widely investigated as prominent actuator candidates in vibration control of structural systems.
On the other hand, each actuator has diverse characteristics, with distinct advantages and disadvantages. In order to achieve diverse and stringent performances under constraints of weight, size, shape, and energy consumption, various functional technologies with a hybrid design philosophy are required. Thereby, by performing a judicious selection, smart structure designers can synthesize numerous classes of hybrid actuation systems to satisfy a broad range of specifications, which cannot be accomplished by developing a single class of actuator system alone. This study exhibits a proof-of-concept investigation on vibration control of a cantilevered beam consisting of an electro-rheological fluid actuator (ERFA) and a piezoelectric film actuator (PFA). Firstly, a hybrid smart structure (HSS) is fabricated by inserting a starch/silicone oil-based electrorheological (ER) fluid into a hollow composite beam and perfectly bonding two piezoelectric films on the outer surfaces of the structure as an actuator and a sensor. Then, control schemes for the ERFA and the PFA are synthesized on the basis of field-dependent frequency responses and neural networks respectively.