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Modified model‐based control of shake tables for online acceleration tracking

Amirali Najafi, Billie F. Spencer

2020Earthquake Engineering & Structural Dynamics28 citationsDOI

Abstract

Summary The interest in shake tables stems from a need to simulate earthquake behavior in laboratory settings. However, the inherent properties and nonlinearities associated with electromechanical and servohydraulic shake tables, combined with issues of table‐structure interaction, make accurate reproduction of earthquake acceleration time histories a challenging problem. The classical approach to control shake tables has been the Transfer Function Iteration (TFI) method. The tuning of the TFI controller is an offline iterative process, conducted using small amplitude ground motions. Effective compensation is not achievable for system nonlinearities that are not projected in the iterative tuning process. To address this problem, researchers have developed online compensation techniques, which can maintain tracking performance for the earthquake signals more effectively. Model‐based controllers (MBC) are a class of online controllers which use an identified model of the shake table‐structure for compensation. The MBC employs feedforward and feedback controllers to ensure that the shake table tracks a specified earthquake ground motion despite the presence of table and structural nonlinearities. However, the feedback controllers in MBC do not always maintain tracking accuracy and can result in loss of robustness when changes occur in the shake table and structure dynamics. This paper introduces a modified model‐based controller (mMBC) for acceleration tracking as an improvement on the existing MBC architecture. A stability condition is introduced to assess the robustness of the new modified control architecture. Through numerical and experimental studies, the improved tracking robustness of the mMBC architecture is demonstrated.

Topics & Concepts

Earthquake shaking tableShakeRobustness (evolution)Control theory (sociology)AccelerationComputer scienceIterative learning controlTransfer functionFeed forwardEngineeringControl engineeringArtificial intelligenceControl (management)Structural engineeringElectrical engineeringMechanical engineeringGenePhysicsChemistryClassical mechanicsBiochemistryHydraulic and Pneumatic SystemsVibration Control and Rheological FluidsGeophysics and Sensor Technology