Controlling and Estimating the Speed of Shaking Table by Fusing Camera, Encoder and Accelerometer using AUKF and Fuzzy Sliding Mode Controller

Abstract

An earthquake is a sudden and destructive natural disaster that often results in unpredictable damage to human life and property. Investigating the effects of earthquakes on buildings and enhancing the seismic performance of structures is a crucial approach to mitigating severe damage during such events. One effective tool in testing the resistance of structures against earthquakes is the use of shaking tables. In this paper, the stabilization and control of an earthquake simulator using a fuzzy sliding mode controller (FSMC), an adaptive unscented Kalman filter (AUKF), and an adaptive extended Kalman filter (AEKF) are presented. These filters employ a recursive technique to effectively adjust the noise covariance by utilizing an adaptation method known as the steepest descent. In the proposed approach, the shaking table states are estimated using an accelerometer, encoder, and camera. These estimated states are then utilized by the AEKF/AUKF to stabilize and control the closed-loop system. A fuzzy sliding mode controller is designed to track the reference input and eliminate external disturbances and noise. In sliding mode control, the occurrence of chattering in the control input is unavoidable. To mitigate this undesired chattering phenomenon, a fuzzy inference mechanism has been employed. The image processing approach has been utilized to measure the displacement online using the camera. The advantages of using the camera include not requiring direct contact with the table, as well as offering a low cost and good accuracy. The performance of the proposed method has been examined using the shaking table at the Research Center of Arak University. The obtained results indicate that the suggested method exhibits a high level of efficiency.