Vision-aided nonlinear control framework for shake table tests
This work addresses the need for more accurate and stable shake table control in earthquake engineering, though it appears incremental as it builds on adaptive control theory with specific experimental validation.
The paper tackled the problem of controlling shake tables for earthquake simulations by developing an adaptive nonlinear control framework that accounts for system nonlinearities and Control-Structural Interaction effects, and results from simulations and experiments with a two-story frame demonstrated its effective application in shake table control.
The structural response under the earthquake excitations can be simulated by scaled-down model shake table tests or full-scale model shake table tests. In this paper, adaptive control theory is used as a nonlinear shake table control algorithm which considers the inherent nonlinearity of the shake table system and the Control-Structural Interaction (CSI) effect that the linear controller cannot consider, such as the Proportional-Integral-Derivative (PID) controller. The mass of the specimen can be assumed as an unknown variation and the unknown parameter will be replaced by an estimated value in the proposed control framework. The signal generated by the control law of the adaptive control method will be implemented by a loop-shaping controller. To verify the stability and feasibility of the proposed control framework, a simulation of a bare shake table and experiments with a bare shake table with a two-story frame were carried out. This study randomly selects Earthquake recordings from the Pacific Earthquake Engineering Research Center (PEER) database. The simulation and experimental results show that the proposed control framework can be effectively used in shake table control.