https://scholar.dgist.ac.kr/handle/20.500.11750/11772 2026-04-11T05:17:39Z https://scholar.dgist.ac.kr/handle/20.500.11750/60180 Title: Long Short-Term Memory Network-Based H∞ Synchronization Control and Anomaly Detection for Cyber-Physical Systems Author(s): Kwon, Hyoeun; Lee, Suwoong; Kwon, Wookyong; Lim, Yongseob; Jin, Yongsik Abstract: In the synchronization of cyber-physical systems (CPSs), modeling the nonlinear dynamics of physical plants is a challenging task. To address this challenge, we propose a novel H∞ controller design method that leverages a data-driven approach to robustly synchronize CPSs and ensure their stability. In the proposed approach, the input-output relationship of the physical system is learned using long short-term memory (LSTM) networks to approximate the unknown dynamics of CPSs. Furthermore, we exploit an effective control scheme for trained LSTM networks to effectively handle the nonlinearity of activation functions. To ensure stability and performance in the convergence of synchronization error, a controller design criterion is derived for the trained LSTM network in terms of linear matrix inequalities, and the controller gain is computed using convex optimization techniques. In addition, we present an anomaly detection algorithm using the proposed method, which can synchronize CPSs and detect abnormal signals without requiring any prior physical model information. Consequently, the stability of the synchronization control system can be ensured, enabling its application to anomaly detection. Finally, the effectiveness of the proposed method is validated through an experiment on a motor control system even in abnormal operating conditions. 2025-10-06T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57881 Title: BEV Image-based Lane Tracking Control System for Autonomous Lane Repainting Robot Author(s): Seo, Junghyun; Jeon, Hyeonjae; Choi, Joonyoung; Woo, Kwangho; Lim, Yongseob; Jin, Yongsik Abstract: In this paper, we present a novel study on a BEV (bird's eye view) image-based lane tracking control system for an autonomous lane repainting robot. Our research introduces a cutting-edge lane detection method based on BEV images, leveraging row-anchor techniques to enhance precision and provide detailed error information for lane tracking algorithms. By utilizing real-time sensor data and advanced deep learning processes, we have successfully implemented a high-performance lane repainting system that minimizes errors and ensures accuracy. Our proposed position-based visual pure pursuit algorithm (PV-PP) plays a crucial role in guiding the lane repainting process with precision and efficiency, ultimately improving the functionality and feasibility of the linear actuator responsible for paint spraying in the real indusrial fields. Through our contributions, including innovative lane detection methods, real-time sensor utilization, and robot control algorithm design, we aim to advance the field of autonomous lane repainting robots and enhance the safety and effectiveness of road maintenance operations. © 2024 IEEE. 2024-10-13T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57555 Title: Event-Triggered Attitude Controller Design for Unmanned Aerial Vehicles under Cyber Attacks Author(s): Han, Seungyong; Guo, Xuyang; Jin, Yongsik; Lim, Yongseob; Kommuri, Suneel Kumar Abstract: This paper proposes an event-triggered attitude controller design method for unmanned aerial vehicles (UAVs) under cyber attacks. Versatile UAVs are capable of transmitting the measurement or receiving control signals through network channels. In the controller design for such UAVs, it is required to consider two distinctive features. The first one is that the strategy of updating the control signal is important for saving the limited energy consumption. The other one is that the UAVs are vulnerably exposed to threats of malicious attacks through network channels. For enhanced energy efficiency and improved stability, the event-triggered mechanism (ETM) and the attitude controller are simultaneously designed. By utilizing Lyapunov- Krasovskii functionals (LKFs), the sufficient conditions for the co-design are derived in terms of linear matrix inequalities (LMIs). For a 3-degrees of freedom (DOF) quadcopter system, the simulation results are presented to validate the proposed attitude control method. © 2024 IEEE. 2024-06-18T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/56799 Title: 베이지안 하이퍼파라미터 최적화 기반 LSTM 타이어 힘 추정기를 이용한 4륜 독립 조향 모델 예측 제어 경로 추종 Author(s): 임성진; 최정민; Sadiq Bilal; 임용섭 2023-11-14T15:00:00Z