Shan Peng, Mengxiang Liu, Ke Zuo, W. Tan, Ruilong Deng
{"title":"Stealthy Data Integrity Attacks Against Grid-tied Photovoltaic Systems","authors":"Shan Peng, Mengxiang Liu, Ke Zuo, W. Tan, Ruilong Deng","doi":"10.1109/ICPS58381.2023.10128033","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128033","url":null,"abstract":"Under the transformation of electric grid towards sustainability and decarbonization, a large number of distributed energy resources including solar photovoltaic (PV) farms are expected to penetrate the grid. As one of the critical state infrastructures, the cybersecurity of PV systems has attracted numerous attention especially with the standardization of grid support services. Various data-driven and model-based intrusion detection systems (IDSs) have emerged for the cybersecurity issue of grid-tied PV systems, among which the stealthy data integrity attacks (DIA) are rarely mentioned. In this paper, we propose a generation scheme of stealthy DIAs, which can bypass two recently proposed (almost state-of-the-art) data-driven and model-based IDSs simultaneously. The attack stealthiness is guaranteed by compromising the sensor measurements cooperatively conforming the physical dynamics of the grid-tied PV system, and meanwhile the attack vector needs to change with an imperceptible speed to avoid steep and observable increase/decrease. Systematical HIL experiments are conducted to verify the stealthiness of the designed stealthy DIA and evaluate its attack impact on PCC voltages.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128036815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Koren, Felix Rinker, Kristof Meixner, Jasminka Matevska, Jörg Walter
{"title":"Challenges and Opportunities of DevOps in Cyber-Physical Production Systems Engineering","authors":"I. Koren, Felix Rinker, Kristof Meixner, Jasminka Matevska, Jörg Walter","doi":"10.1109/ICPS58381.2023.10128073","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128073","url":null,"abstract":"DevOps is a set of practices that combines software development and operations to enable a continuous software product life cycle to improve the quality of software systems. Although DevOps is considered successful for typical software systems, it has not yet been widelyadopted in the area of - Physlcal Production Systems (CPPSs) that integrate physical components with computer-based control and communication systems. Related to industrial settings, many new challenges arise, like long-term investments, missing flexibility in asset-heavy production environments, and the inherent physicality of hardware. This paper examines the use of the DevOps methodology in the manufacturing domain. It identifies and discusses the unique challenges and describes first solution proposals to overcome those challenges, based on literature and experiences from the industry. The article provides useful guidance to researchers and practitioners on potential pitfalls and exciting opportunities.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114674809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Data Enhancement Strategy for Multi-Agent Cooperative Hunting based on Deep Reinforcement Learning","authors":"Zhenkun Gao, Xiaoyan Dai, Meibao Yao, Xueming Xiao","doi":"10.1109/ICPS58381.2023.10128078","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128078","url":null,"abstract":"Cooperative hunting is a typical and significant scene to study multi-agent behaviors, where conventional control strategies are difficult to cope with, due to its high dimensionality of state space and locality of communication. Reinforcement learning provides a framework and a set of tools for this issue by trial-and-error interactions with the environment. Though promising, it often requires a large number of empirical sample data to learn effective hunting strategies, leading to low sample efficiency, understood as the training episodes required for the agent to learn effective behavior strategies. To improve the sampling efficiency, we propose a data enhancement strategy integrated in the execution (CTDE) training framework to train the multi-agent system. The data enhancement strategy is based on a state transfer dynamics model to generate additional predicted data, which we called dynamic prediction model, combined with the empirical data by interacting with the environment, for higher sample efficiency. The simulation results on the Webots platform show that our method outperforms some state-of-the-art methods, such as MAPPO, with high data sample efficiency.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"371 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114867144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdul Rehan Khan Mohammed, Jiayi Zhang, Benjamin Silverstone, Ahmad Bilal
{"title":"Observer-Based Robust Adaptive Control for the Synchronization of Uncertain Multiple Robot Manipulators","authors":"Abdul Rehan Khan Mohammed, Jiayi Zhang, Benjamin Silverstone, Ahmad Bilal","doi":"10.1109/ICPS58381.2023.10127998","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10127998","url":null,"abstract":"Robotics research in today's era is mainly focused on multi-robot manipulator systems, since a single robot manipulator is no longer the best solution for several applications such as robotic exploration in hazardous environments, automated production plants and space. Trajectory planning of the robotic manipulators is significant in these applications. The performance of the manipulator in this endeavor is influenced by parametric uncertainties, non-parametric uncertainties, unmodeled dynamics and external disturbances. These influences in addition to the operational errors, will result in the reduced life span of the manipulators. This paper proposes a robust adaptive control strategy with a time-varying high-gain observer to synchronize multiple robot manipulators. The objective is to minimize undesirable disturbances and make the system follow a chosen reference model dynamics or trajectory. The Lyapunov method is used to derive the tuning parameters and the stability of the proposed scheme. Simulation results carried out on three identical two-degree-of-freedom robot manipulators show that the proposed robust adaptive synchronization control scheme achieves boundedness for all the closed-loop signals and ensures convergence of both the tracking and synchronization errors.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data Modeling Method and Application Practice on Identifiable Digital Object","authors":"Bin Xie, Juan Tian, Cheng Chi, Yang Liu","doi":"10.1109/ICPS58381.2023.10127990","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10127990","url":null,"abstract":"The intelligent optimization closed loop of “data-driven + industrial knowledge” is applied to the entire industrial chain and the entire value chain of the industry. The volume of industrial data is gradually increasing rapidly, and the content of industrial data is constantly enriched. A standardized industrial data model becomes the key to accelerating the precipitation, abstraction and reuse of industrial information. Based on the open and shared network ecosystem, this paper proposes the creation method and multi-role use process of the identifiable digital object (IDO) data model, and selects a specific industry product life cycle management scenario for data evaluation and analysis, and verifies the establishment of IDO It provides a more effective and complete method for users to share necessary industry information within or across enterprises.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122141777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Resilient Predictive Control of Constrained Connected and Automated Vehicles under Malicious Attacks","authors":"Henglai Wei, Yan Wang, Jicheng Chen, Hui Zhang","doi":"10.1109/ICPS58381.2023.10128093","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128093","url":null,"abstract":"In this paper, we present a novel resilient distributed model predictive control (RDMPC) framework for the con-strained Connected and Automated Vehicles (CAV) in the pres-ence of $F$ -local malicious attacks. The proposed framework aims to ensure constraint satisfaction and identify malicious attacks using previously broadcast information and a convex set, referred to as the ”resilience set.“ Compared to the well-known Mean Subsequence Reduced (MSR) algorithms that require (2F + 1)-robust graphs, the proposed approach significantly reduces the required robustness level to (F + 1)-robust graph. Our simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of malicious attacks on constrained CAVs while ensuring constraint satisfaction. Overall, the proposed RDMPC framework contributes to the field of resilient platoon control for CAVs and has potential implications for improving the reliability and security of CAVs in real-world scenarios.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130960207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ni Zhang, Xiaowei Jiang, Xian-He Zhang, Lee-Shin You
{"title":"Impulsive Formation Control of Nonlinear Leader-Following Multi-agent Systems with Input Saturation*","authors":"Ni Zhang, Xiaowei Jiang, Xian-He Zhang, Lee-Shin You","doi":"10.1109/ICPS58381.2023.10128008","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128008","url":null,"abstract":"Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130739473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sunil Rao, Deep Pujara, A. Spanias, C. Tepedelenlioğlu, Devarajan Srinivasan
{"title":"Real-time Solar Array Data Acquisition and Fault Detection using Neural Networks","authors":"Sunil Rao, Deep Pujara, A. Spanias, C. Tepedelenlioğlu, Devarajan Srinivasan","doi":"10.1109/ICPS58381.2023.10128030","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128030","url":null,"abstract":"Continuous real-time solar system monitoring for fault detection and classification can improve solar panel efficiency and overall output. In this study, we developed and implemented a real-time PV fault detection system based on machine learning. The system was implemented on an 18kW testbed facility which consists of 104 solar panels located at the ASU Research Park. Each solar panel is connected to a smart monitoring device (SMD) which obtains real-time voltage and current measurements. SMDs are attached to each panel and transmit all the acquired data to a server that is connected to the internet. We implement fault detection using real-time measurements and various neural network architectures. We train and test both fully connected and dropout neural networks with different dropout regularization. We use both a real-time dataset and a synthetic dataset and present comparative results. We train and classify for the following conditions: soiled panels, shaded and degraded panels, and standard test conditions.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117054346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Novel Adaptive DC Voltage Droop Control for MMC-MTDC Considering Local Electric Variables","authors":"Ji Sun, Jianhua Liu, Jianlong Qiu, Xiangyong Chen","doi":"10.1109/ICPS58381.2023.10128058","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128058","url":null,"abstract":"To solve the problem that the multi-terminal HVDC system which adopts fixed coefficient droop control has unacceptable DC voltage deviation and terrible power distribution ability, an adaptive DC voltage droop controller is designed which can give consideration to both DC voltage control ability and power distribution ability. Firstly, the basic structure of MTDC and MMC is introduced. Then a novel adaptive DC voltage droop controller is designed based on the local electric variables of the converters running in droop control mode. Finally, $mathrm{a}pm 200$ kv four-terminal HVDC system simulation model has been built in PSCAD/EMTDC, the adaptive voltage droop control strategy introduced in this dissertation is validated. The simulation results show that compared with the traditional droop controller, the proposed adaptive DC voltage droop controller has stronger DC voltage control ability when the power flowing through a converter is greater than the reference, or contrariwise, the controller has better power distribution ability.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123448562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"YOLO V5-MAX: A Multi-object Detection Algorithm in Complex Scenes","authors":"Xingkun Li, Guangyu Tian, Zhenghong Lu, Guojun Zhang","doi":"10.1109/ICPS58381.2023.10128009","DOIUrl":"https://doi.org/10.1109/ICPS58381.2023.10128009","url":null,"abstract":"The target detection of autonomous ground vehicles (AGVs) has the problem of few and slow object categories, which will cause great safety problems for AGVs. This paper proposes a YOLO v5-MAX algorithm to deal with the problem of a few types of object detection in complex scenes, e.g., city traffic jam, pedestrians crossing the road, running red lights, overtaking, merging, etc. The proposed algorithm consists of two parts. Firstly, the proposed algorithm uses YOLO v5s as the initial network model to train the vehicle detection model, which is used to detect the three categories of cars, buses, and trucks. Secondly, based on the first part, a Neck network and Head output layer are added to the proposed algorithm to detect four categories of person, bike, motor, and rider. In this paper, the most commonly used YOLO v5 object detection network is taken as an example to verify the effectiveness and realizability of our innovation. Of course, our method can also be applied to other object detection models, providing a theoretically feasible method for multi-object detection in the future. Finally, after the proposed algorithm is trained, it is deployed to Jetson TX2 for actual AGVs detection experiments. The experimental results show that the detection types and detection speed of the proposed algorithm have been greatly improved.","PeriodicalId":426122,"journal":{"name":"2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126694363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}