2023 31st Mediterranean Conference on Control and Automation (MED)最新文献_第2页

Cascaded Disturbance Compensation for MPC-based Autonomous Vehicle Guidance 基于mpc的自动驾驶车辆制导级联干扰补偿

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185834

Arash Jalilian, Norman Schwarz, Andreas Völz, Robert Ritschel

引用次数: 0

Tube-based Nonlinear MPC of an Over-actuated Marine Platform for Navigation and Obstacle Avoidance using Control Barrier Functions 基于控制障碍函数的超驱动船舶导航与避障平台非线性MPC

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185841

Spyridon Syntakas, K. Vlachos

引用次数: 0

Mixed FTS/H∞ Control for Nonlinear Quadratic Systems Subject to Norm-Bounded Disturbances 范数有界扰动下非线性二次系统的混合FTS/H∞控制

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185787

A. Merola, Francesca Nesci, Donatella Dragone, F. Amato, C. Cosentino

引用次数: 0

Modeling and control of a hybrid PV-T collector using machine learning 基于机器学习的混合PV-T集热器建模与控制

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185721

Z. Abdin, A. Rachid

引用次数: 0

Observer-based state feedback air path control for a turbocharged diesel engine with EGR and VGT 带EGR和VGT的增压柴油机观测器状态反馈风道控制

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185863

Oussama Djadane, Salama Makni, A. Hajjaji

引用次数: 0

An interlaced co-estimation technique for batteries 电池的交错共估计技术

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185840

Elisa Mostacciuolo, L. Iannelli, Silvio Baccari, F. Vasca

引用次数: 0

Unsupervised Anomaly Detection for Multivariate Incomplete Data using GAN-based Data Imputation: A Comparative Study 基于gan的多变量不完全数据的无监督异常检测:比较研究

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185791

Kisan Sarda, A. Yerudkar, C. D. Vecchio

{"title":"Unsupervised Anomaly Detection for Multivariate Incomplete Data using GAN-based Data Imputation: A Comparative Study","authors":"Kisan Sarda, A. Yerudkar, C. D. Vecchio","doi":"10.1109/MED59994.2023.10185791","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185791","url":null,"abstract":"With the increasing interconnectivity of cyber-physical systems (CPSs) in various fields, such as manufacturing plants, power plants, and smart networked systems, large amounts of multivariate data are generated through sensors and actuators, also other data sources such as measurements and images. This paper focuses on the anomaly detection (AD) problem, also known as fault detection or outlier detection, depending on the type of dataset, which involves identifying anomalous values in the dataset using analytical methods. However, datasets often contain missing values, which can lead to incorrect outcomes and affect the availability of anomalous samples that are fewer in amount, making incomplete datasets. Therefore, a generalized AD method is proposed for incomplete datasets, which involves two steps: data imputation (DI) to obtain complete datasets using GAN and later AD for the complete datasets. While statistical-based imputation methods are commonly used, they do not consider data distribution for datasets with anomalous samples. The capabilities of GANbased DI are tested under different hyperparameter settings and percentages of missing values. The AD problem is then addressed using seven unsupervised anomaly detection methods on six different datasets, including a real dataset from a steel manufacturing plant in Italy. Each dataset is analyzed to determine which DI and AD method combination performs the best. The results show that GAN-imputed data provides the best DI performance, while the reweighted minimum covariance determinant (RMCD) method offers the overall best AD results combined with GAN.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114499416","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}

引用次数: 0

Towards efficient traffic state estimation using sparse UAV-based data in urban networks 城市网络中基于无人机稀疏数据的高效交通状态估计

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185776

Kyriacos Theocharides, C. Menelaou, Y. Englezou, S. Timotheou

{"title":"Towards efficient traffic state estimation using sparse UAV-based data in urban networks","authors":"Kyriacos Theocharides, C. Menelaou, Y. Englezou, S. Timotheou","doi":"10.1109/MED59994.2023.10185776","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185776","url":null,"abstract":"Traffic state estimation (TSE) is a challenging task due to the collection of sparse and noisy measurements from fixed points in the traffic network. Unmanned Aerial Vehicles (UAVs) have been gaining popularity as traffic sensors due to their ability to monitor a number of important traffic parameters over space and time. In this work, we develop a novel UAV-based sensing architecture which provides sparse, noisy measurements of traffic densities and transfer flows of the traffic network. Assuming free-flow conditions, we construct a Kalman filter approach that utilises knowledge of regional split ratios along with the UAV-based measurements. To avoid the assumption of known split ratios, we further develop a weighted least-squares optimization approach that minimizes measurement and process errors over a moving horizon window subject to linear traffic dynamics to accurately estimate traffic densities. We compare the UAV-based sensing architecture to an all-measurement method where we assume that measurements for all traffic densities and transfer flows are available at every time-step. Results show that the UAV-based sensing architecture compares favourably to the all-measurement scenario and the proposed optimization based estimator achieves similar results to the Kalman filter, even when regional split ratios are unknown.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117161265","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}

引用次数: 0

Model Predictive Control with Adaptive PLC-based Policy on Low Dimensional State Representation for Industrial Applications 基于自适应plc的低维状态表示模型预测控制在工业中的应用

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185716

Steve Yuwono, Andreas Schwung

{"title":"Model Predictive Control with Adaptive PLC-based Policy on Low Dimensional State Representation for Industrial Applications","authors":"Steve Yuwono, Andreas Schwung","doi":"10.1109/MED59994.2023.10185716","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185716","url":null,"abstract":"In the modern era of manufacturing automation, the integration of sensor technology into the system ensures that data acquisition and analysis from complex systems become more efficient than ever. With the support of such developments, artificial intelligence-powered control in industrial control domains gains popularity and enhances the traditional human-based PLC control, where the machines can monitor themselves, learn from the experience, and make their own decisions. However, despite advances in sensor technologies, there are some limitations of the current applications of sensors in industries, for instance, sensors for observing the current status of the system often provide Boolean output data instead of continuous output. Therefore, such limitation forms a low dimensional state representation of the system, which could be problematic to develop a self-control policy, e.g. using a model-free deep reinforcement learning. In this paper, we present an effective model predictive controller with adaptive PLC-based policy on low dimensional state representation specifically for industrial control domains. First, we learn the model of the production system using the deep learning method to get the representation of the system dynamics, in case its digital representation is not available. Second, we set up a native implementation of model predictive control. Third, we enhance the model predictive control with adaptive PLC-based policy. The proposed method is implemented into a bulk good system showing its potential to self-optimize the system by satisfying the production objective without overflow and low power consumption.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116031978","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}

引用次数: 0

Cybersecurity Oriented Architecture to Ensure the Autonomous Vehicles Communication 面向网络安全的自动驾驶汽车通信保障体系结构

2023 31st Mediterranean Conference on Control and Automation (MED) Pub Date : 2023-06-26 DOI: 10.1109/MED59994.2023.10185802

Athanasios Sersemis, Alexandros Papadopoulos, Georgios Spanos, Antonios Lalas, K. Votis, D. Tzovaras

{"title":"Cybersecurity Oriented Architecture to Ensure the Autonomous Vehicles Communication","authors":"Athanasios Sersemis, Alexandros Papadopoulos, Georgios Spanos, Antonios Lalas, K. Votis, D. Tzovaras","doi":"10.1109/MED59994.2023.10185802","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185802","url":null,"abstract":"The topic of in-vehicle and V2X communication in autonomous vehicles consists of a variety of different communication protocols, mechanisms, and devices. The implementation and cooperation between these entities and protocols in such a complex system is a rigorous and complicated process that should not only be efficient, robust, flexible, and scalable, but also secure. The security of critical systems such as autonomous vehicles requires a deep understanding of all the individual and distinct components that compose the system. This paper presents a cybersecurity architecture having as purpose to shield the communication security in the autonomous vehicles. For this reason, several well-established cybersecurity tools (e.g. Keycloak, Cloudflare) and communication mechanisms (e.g. MQTT, Kafka) have been combined in this architecture along with a novel statistical-based Intrusion Detection System. All the aforementioned cybersecurity defense mechanisms were selected to protect the entire system pipeline and meet the requirements for Confidentiality, Integrity, and Availability regarding vehicle communication. To test the performance of the proposed architecture abnormal data have been injected to the system and the results from the experiments conducted highlighted that the proposed solution can achieve its purpose of increased cybersecurity.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"12 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123724778","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}

引用次数: 0