# Vibration Isolation of a Surveillance System Equipped in a Drone with Mode Decoupling
## 1 Introduction  
Technological enhancements in various research fields have opened the door to the Fourth Industrial Revolution [1]. These technologies include artificial intelligence, the Internet of Things, big data analytics, augmented reality, additive manufacturing, cloud computing, smart sensors, robotics, and system integrations [2]. In particular, smart sensors, robotics, and system integration directly connect cybernetics to human life. Specifically, integrated robotic systems equipped with smart sensors and artificial intelligence can execute various challenging and repeated missions, freeing humans from executing these tasks. Traditional research fields in mechatronics should properly collaborate to construct state-of-the-art technologies mentioned above to develop intelligent (and autonomous) robotic systems. Mechatronics is an interdisciplinary branch of engineering focusing onthe engineering of electronic, electrical, and mechanical systems, including a combination of robotics, electronics, computer engineering, telecommunication, signal processing, and product engineering. This implies that mechatronics is an essential foundation in creating intelligent robotic systems for the Fourth Industrial Revolution. Vibration isolation and control with system identification plays a critical role among many mechatronics branches in conducting missions of intelligent robotic systems because excessive vibration degrades the performance of control, monitoring, and diagnosis of a robotic system of interest. Various methods have been studied for vibration isolation and control with system identification [3]. The observer/Kalman filter identification technique was proposed for vibration control of smart structures [4]. An inverse model of a magnetorheological damper using optimal neural network and system identification was proposed [5]. A method to identify a nonlinear system [6] and proper model selection procedure was also suggested [7]. A vibration of the end-effector for industrial robots was recently identified through a linear system identification method; after that, a vibration was reduced with the linear quadratic-Gaussian controller [8]. Moreover, an active vibration isolation system was presented for vibration isolation of a surveillance system [9]. A robust vibration control systems with advanced control strategies accounting for system uncertainty have been also studied to reduce vibration occurred in unmanned aerial vehicles, i.e., drones [10; 11; 12]. These studies have provided useful information for the vibration control and isolation of robotic systems. However, previous studies on vibration control of intelligent robotic systems have two limitations. First, they estimated system dynamics through experiments or used a simple decoupled model for system identification, suggesting no correlation between each degree of freedom and other coordinates. This simple approach can rapidly and easily identify a robotics system. However, the model has limited accuracy in predicting complex coupling dynamics of intelligent robotic systems. Therefore, it is difficult to ensure the high performance of vibration isolation. More importantly, the coupling effects of vibration on resonance significantly tremble sensor measurements, suggesting that a robot cannot execute the mission. Second, most studies on vibration problems in intelligent robotic systems addressed active vibration systems with novel control approaches. Active control of vibration enhances the vibration performance compared to passive vibration isolators in most cases. Moreover, this approach is effective in combating external disturbances, uncertainties, and environmental influences that act on the drone. However, this approach increases the complexity of vibration isolators and weight and power consumption of smart mobility because it needs axillary components including actuators and a controller for vibration isolation.