Adaptive Neural Network Event-Triggered Control for a High-Rise Building With Active Mass Damper
提出一种自适应神经网络事件触发控制方法，用于抑制高层建筑在不确定性下的振动，通过数值仿真和实验验证了其有效性。
In this article, we propose an adaptive neural network event-triggered control (ETC) to suppress the vibration of a high-rise building under uncertainty. This neural network efficiently handles unmodeled components in the system and approximates unknown nonlinear functions. An ETC mechanism with a relative threshold strategy is introduced, balancing the control effectiveness of the active mass damper (AMD) and extending operational lifespan. The ultimate boundedness of the system is verified using the Lyapunov direct method, ensuring convergence of vibration displacement and acceleration toward zero. The efficacy of this control scheme is demonstrated through detailed numerical simulations and experimental analyses.