Vision-aided nonlinear control framework for shake table tests

TL;DR

This paper introduces an adaptive nonlinear control framework for shake table tests that accounts for system nonlinearity and control-structure interaction, validated through simulations and experiments.

Contribution

It proposes a novel adaptive control approach incorporating loop-shaping controllers to handle unknown parameters and nonlinearities in shake table systems.

Findings

Effective control of shake table systems demonstrated in simulations.

Experimental validation with a two-story frame confirms feasibility.

Framework accommodates unknown mass variations and nonlinear effects.

Abstract

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.