In this paper, an intelligent control methodology is proposed to mitigate earthquake vibrations in a building.
The structure object of study is a 10-story building whose base is isolated by means of a passive actuator and
an MR damper. The system has uncertain parameters and nonmeasurable variables that must be accounted for
in order to gain good control performance. Besides, the system is subject to unknown perturbations (incoming
earthquakes). An adaptive backstepping controller is designed to generate the actuator control signal based
on the base velocity and displacement measurements as well as on the dynamics of the base isolation system.
Uncertainty in structure stiffness and damping coefficients are compensated by parameter adaptation. The MR
damper can be modeled by the well known Bouc-Wen model. However, this model contains an unmeasurable
variable, z, that describes the hysteretic behavior, so it must be estimated. A neural network approximator
is proposed to estimate the unmeasurable variable. This way, the hysteresis effect is modeled by the neural
network. The control performance is verified by simulations performed in MATLAB/Simulink using common
earthquakes such as those of El Centro and Taft.
This paper presents the results of modeling a shear-mode MR damper. The prototype damper consists of two steel parallel plates and in the middle, there is a paddle covered by foam saturated with MR fluid. The force is produced when the paddle is in motion and the magnetic field generated by a coil in one end of the device
reaches the fluid. Several response forces were captured at different displacement excitations and magnetic field levels. The goal is to find a relationship between the velocity, the control voltage (inputs of the system) and the force generated by the device. The results of predicting the force using the Bingham, Bouc-Wen and Hyperbolic
Tangent based models are compared and the suitability of these models is discussed.