Design and Implementation of Genetic-Fuzzy Controller for an Electric Seismic Shake Table

A supervisory genetic-fuzzy controller is proposed for the motion control of an electric shake table, LARZA, in this paper. The controller comprises of two loops, an inner PI loop and a genetic-fuzzy supervisor. The fuzzy controller is devised, based on the prior experience, such that the tracking error is minimized. Furthermore, for optimizing the fuzzy supervisor controller, a genetic algorithm is utilized. For this purpose, a mathematical model is developed for the shake table. Moreover, the model is validated based on the test data. The parameters of the supervisor controller are then tuned based on the model response variables using the genetic optimization approach. The controller is implemented in the shake table and its performances is studied. The test results reveal the effectiveness of the proposed controller in decreasing displacement, velocity, and acceleration tracking errors for two sample earthquakes.