In this paper, Design of a fuzzy sliding mode controller (FSMC) with adaptive extended Kalman filter (AEKF) for controlling a shake table system with electric actuator and ball-screw mechanism. Due to the uncertainties regarding the model parameters and the noise of the More
In this paper, Design of a fuzzy sliding mode controller (FSMC) with adaptive extended Kalman filter (AEKF) for controlling a shake table system with electric actuator and ball-screw mechanism. Due to the uncertainties regarding the model parameters and the noise of the data of the two encoder and accelerometer sensors, there are many problems in controlling this system. Therefore, it is crucial to employ a non-precise model-based controller and a nonlinear adaptive filter. The fuzzy sliding mode control and Extended Kalman filter are a good way to control this system. In sliding mode control, chattering at the control input is inevitable. In this paper, a simple fuzzy inference mechanism is used to reduce the undesirable phenomenon of chattering by correctly estimating the upper bound of uncertainty. In the following, a recursive method is used to determine the system and measurement noise covariance matrices. The data of the two encoder and accelerometer sensors are combined in the adaptive extended Kalman filter and the results in noise elimination and parameter estimation are investigated. Linear speed feedback available through the Kalman filter is used to stabilize and control the closed loop system. The end is examined to check the performance of the control structure provided by the seismic table test. The results show that the proposed method is very effective.
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