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Dynamical stability analysis of MDOF real-time hybrid system

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Abstract Real-time hybrid simulation is an advanced testing methodology to evaluate structural responses under realistic operating conditions. Typically, the real-time hybrid system uses actuation and control system to apply load… Click to show full abstract

Abstract Real-time hybrid simulation is an advanced testing methodology to evaluate structural responses under realistic operating conditions. Typically, the real-time hybrid system uses actuation and control system to apply load and motion boundary conditions on the experimental substructure. The inherent system dynamics present phase lags, in addition to the communication delays, that both cause negative damping in the real-time hybrid system. In this study, the dynamic equation of motion is derived for a generalized multiple-degree-of-freedom hybrid system. The negative damping effect is quantified, which depends not only on the actuator motion control performance, but also more importantly on the partition between the numerical and experimental substructures (i.e. how the stiffness and mass are assumed in both substructures). It is demonstrated the worst-case hybrid substructure partition may have very narrow stability margin in its tolerance of any system delay. The proposed equation of motion gains system level understanding of any arbitrary hybrid substructure partition; thus allow both frequency domain system analysis and time domain evaluation. The benchmark problem is studied to validate the proposed equation of motion compared with the virtual testing method, both approaches show excellent correlation. These pre-testing assessments could establish quantitative predictive measures about the system stability limit and performance criteria. Thus they are very important in the early design stage of a feasible real-time hybrid implementation, help reduce the risks of unintended physical testing responses.

Keywords: system; time; hybrid system; time hybrid; real time; motion

Journal Title: Mechanical Systems and Signal Processing
Year Published: 2019

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