Online adaptive neuro-fuzzy controller design to attenuate the seismic responses in a 20-story benchmark structure

In the present research, design of a strong and online adaptive controller in the active cable control system is discussed to overcome the earthquake vibrations of multi-story buildings. Considering all variables as unknown, this study introduces a new type 2 adaptive neuro-fuzzy controller. Using the MLP neural network (multi-layer perceptrons), Jacobian and the structural system estimation are extracted. This estimated structural system model is implemented into the online controller system in the next step. Adaptive controllers are tuned using a post-propagation algorithm and Extended Kalman Filter and are thus able to control and tune the controllers and the cable system. In this method, a PID controller is also used, which increases the strength and stability of the adaptive neural-fuzzy controller system two against earthquake vibrations. The superiority of the proposed controller system over an online simple adaptive controller is also demonstrated. This controller is utilized as an implicit reference model. In this proposed method, Extended Kalman Filter is innovatively used to tune online controllers. In this research, the performance of both controllers is investigated under the far and near fault field pressures. Based on the numerical results, the adaptive neural-fuzzy controller performs about 21% better than the online simple adaptive controller in minimizing the seismic responses of the structure during an earthquake and reaching the control criteria when the parametric characteristics of the structure change.