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A novel control architecture for marginally stable dynamically substructured systems

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Abstract Dynamic substructuring is a popular hybrid testing technique that mixes physical testing with numerical simulations. Research in this domain has mainly focused so far on structures and decomposition strategies… Click to show full abstract

Abstract Dynamic substructuring is a popular hybrid testing technique that mixes physical testing with numerical simulations. Research in this domain has mainly focused so far on structures and decomposition strategies that result in asymptotically stable systems. However, several cases fall outside this family, with either the physical or the numerical part being only marginally stable post-decomposition. A typical example of such scenario is when a mass is split between physical and numerical parts. For generic structures with marginally stable subsystems, current techniques often fail to deliver satisfactory performance or require modifications to the original structure. In this paper, analytical results – valid for generic structures – are derived to highlight the potential shortcomings of current techniques and to motivate the proposal for a novel control architecture to enable dynamic substructuring with marginally stable subsystems. By selecting a suitable set of signals for control design and by augmenting the control strategy via a local controller for the marginally stable subsystem, the proposed method allows accurate hybrid testing without relying on the modifications of the original structure that classical approaches require. This technique is suitable for all of the mass split structures, and indeed for all marginally stable structures, which means that the results are applicable to a wide range of hybrid testing problems. A simulated benchmark problem of hybrid testing for vibrating structures is included to demonstrate the effectiveness of the proposed technique.

Keywords: hybrid testing; novel control; control; marginally stable; control architecture

Journal Title: Mechanical Systems and Signal Processing
Year Published: 2020

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