Seismic Vibration Control of Building Structures Using RBF Neural Network-Enhanced Sliding Mode Control

In this study, we explore an advanced approach for controlling the vibrations in building structures induced by seismic waves. We use Radial Basis Function (RBF) neural networks to approximate the nonlinear uncertainties inherent in building structures. By approximating these nonlinearities, we are able to design an effective Sliding Mode Control (SMC) system. The proposed control strategy leverages the approximation capabilities of RBF neural networks to enhance the robustness and performance of the sliding mode controller. Simulation results demonstrate that the RBF neural network-enhanced SMC can effectively suppress seismic-induced vibrations, ensuring the stability and integrity of building structures during seismic events. This method offers a promising solution for improving the seismic resilience of buildings through intelligent control techniques.