## 1 Introduction
### Active Mass Damper  
An active mass damper (AMD)--which connects a moving mass block to the structure to be controlled through a hydraulic actuator--has been applied to mitigate vibration responses due to dynamic loads. The hydraulic actuator of an AMD system aims to reproduce desired force computed from the structural control algorithm through generating acceleration response of the mass block. Herein, the accuracy of the achieved control force is related to the acceleration control performance of the movement of the AMD mass block. Figure 1 illustrates the mathematical model of a building with an active mass damper in which \(m_{1}\), \(c_{1}\), and \(k_{1}\) represent the mass, damping coefficient, and stiffness of the primary structure; \(m_{2}\) is the mass of the AMD mass block; \(u\) is the control force; and \(f\) is the equivalent external force. Numerous studies have been focused on controller synthesis and analysis for computing desired force for AMD installed on a structure for the past decades. Various control algorithms for AMD have been proposed and validated such as the complete-feedback control algorithm which incorporates displacement, velocity and acceleration measurements as the feedback signal [2]; the linear-quadratic regulator (LQR) which requires displacement and velocity feedback [3]; and the linear-quadratic Gaussian (LQG) controller with acceleration feedback control [4; 5]. Meanwhile, the neural network has been proposed and become an alternative to replace the control algorithm for structural control [6]. [PERSON] et al. used the back-propagation algorithm proposed by [PERSON] et al. [7] for training neural network in structural control [8]. [PERSON] and [PERSON] applied the neural network control algorithm to a nonlinear three degrees-of-freedom steel frame model [9]. [PERSON] et al. proposed an optimal control algorithm using neural networks through minimization of the cost function [10]. [PERSON] et al. used neural networks with a training algorithm for active pulse structural control without the trial-and-error selection of the learning rate [11]. [PERSON] et al. applied a neural network control for nonlinear bridge systems with earthquake excitation [12]. [PERSON] et al. proposed a smart active control framework which utilized fiber Bragg grating sensors and neural networks [13]. In addition, the neural network controller has been applied to reduce the wind-induced vibration of a tall building through an active tuned mass damper [14]. An AMD controller used neural network based on optimal control method for a cable-stayed bridge structure [15]. Most of these studies have been focused on numerical simulation without experimental validation. Recently, [PERSON] and [PERSON] [16] proposed a neural network with modified Newton method for structural control with an AMD and validated the control performance through shake table testing. Experimental results have shown that the neural network controller is effective in damping control applications. However, the mass ratio of the AMD used in the experiment was more than 20% which is not realistic in real implementation for buildings. Practical experimental studies regarding to the AMD seismic control performance achieved by using machine learning are considered limited.