Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185867
A. Yurchenkov, A. Kustov
In this paper, a discrete time-varying model of sensors network is considered. The external input belongs to the class of sequences of random vectors with bounded anisotropy of the extended vector. The anisotropy-based analysis of the system includes the analysis for the multiplicative noise systems and the boundedness criterion of the anisotropic norm. The considering problem concerns the selection of the estimator, which one guarantees the boundedness anisotropic norm. It is demonstrated how to reduce considering problem to convex optimization one
{"title":"Anisotropy-based Approach of Estimating for Sensors Network with Nonzero Mean of Input","authors":"A. Yurchenkov, A. Kustov","doi":"10.1109/MED59994.2023.10185867","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185867","url":null,"abstract":"In this paper, a discrete time-varying model of sensors network is considered. The external input belongs to the class of sequences of random vectors with bounded anisotropy of the extended vector. The anisotropy-based analysis of the system includes the analysis for the multiplicative noise systems and the boundedness criterion of the anisotropic norm. The considering problem concerns the selection of the estimator, which one guarantees the boundedness anisotropic norm. It is demonstrated how to reduce considering problem to convex optimization one","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"258 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":"130387885","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185874
Nikolaos Sarantinoudis, George J. Tsinarakis, L. Doitsidis, N. Tsourveloudis, G. Arampatzis
This paper presents a bibliometric analysis of the research literature on potential applications of digital twins in autonomous vehicles, aiming to identify its main features, the current research trends and their evolution and potential gaps for future studies. The set of publications under study is collected through the most popular scientific databases by performing targeted queries and after removing erroneous entries. Different types of analysis (trend analysis, co-occurrence analysis and citation analysis) are performed and the results obtained are presented through graphs and tables, discussed to extract useful conclusions and widened to propose future extensions and suggestions for the involved stakeholders.
{"title":"Bibliometric Analysis on Applications of Digital Twins in Autonomous Vehicles","authors":"Nikolaos Sarantinoudis, George J. Tsinarakis, L. Doitsidis, N. Tsourveloudis, G. Arampatzis","doi":"10.1109/MED59994.2023.10185874","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185874","url":null,"abstract":"This paper presents a bibliometric analysis of the research literature on potential applications of digital twins in autonomous vehicles, aiming to identify its main features, the current research trends and their evolution and potential gaps for future studies. The set of publications under study is collected through the most popular scientific databases by performing targeted queries and after removing erroneous entries. Different types of analysis (trend analysis, co-occurrence analysis and citation analysis) are performed and the results obtained are presented through graphs and tables, discussed to extract useful conclusions and widened to propose future extensions and suggestions for the involved stakeholders.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"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":"134345292","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185703
Grigoris Michos, George C. Konstantopoulos, P. Trodden, V. Kadirkamanathan
This paper proposes a robust control scheme for isolated AC Microgrids, where each node is connected locally to a constant power load (CPL). Contrary to many approaches in the literature, we consider the explicit model of the inverter dynamics and separate the overall system into two parts; a nominal subsystem parametrized by a nominal load and an error subsystem describing the difference between the true and the nominal voltage, resulting from perturbations of the load demand. In the presented analysis, we investigate the non-linear structure of the CPL in order to analytically describe its geometric effect on the network dynamics. We exploit this information to propose mild conditions on the tuning parameters such that a positive invariant set for the error dynamics exists and the distance between the true and the nominal voltage trajectories is bounded at all times. We demonstrate the properties of the proposed control scheme in a simulated scenario.
{"title":"Control of Isolated AC Microgrids with Constant Power Loads: A Set Invariance Approach","authors":"Grigoris Michos, George C. Konstantopoulos, P. Trodden, V. Kadirkamanathan","doi":"10.1109/MED59994.2023.10185703","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185703","url":null,"abstract":"This paper proposes a robust control scheme for isolated AC Microgrids, where each node is connected locally to a constant power load (CPL). Contrary to many approaches in the literature, we consider the explicit model of the inverter dynamics and separate the overall system into two parts; a nominal subsystem parametrized by a nominal load and an error subsystem describing the difference between the true and the nominal voltage, resulting from perturbations of the load demand. In the presented analysis, we investigate the non-linear structure of the CPL in order to analytically describe its geometric effect on the network dynamics. We exploit this information to propose mild conditions on the tuning parameters such that a positive invariant set for the error dynamics exists and the distance between the true and the nominal voltage trajectories is bounded at all times. We demonstrate the properties of the proposed control scheme in a simulated scenario.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"1955 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":"129330863","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185780
Christos Anagnostopoulos, A. Lalos, P. Kapsalas, Duong Nguyen Van, C. Stylios
Simultaneous Localization and Mapping (SLAM), which is characterized as a core problem in autonomous vehicles, involves the estimation of the vehicle’s position and the concurrent building of the map of the environment. The use of deep learning-based feature extractors has gain increasing popularity since they possess the ability to extract reliable and repeatable features from raw sensor data. However, the performance of deep learning-based approaches varies depending on the application, environmental conditions, and the type of implemented technology. In this paper, we evaluate the performance of several deep learning-based feature extractors integrated into a SLAM system, using as input real and synthetic data, which implement common odometry problems. To our knowledge, this is the first work that benchmarks the accuracy of deep-learning based algorithms in estimating the vehicle’s trajectory in specific odometry corner cases.
{"title":"Reviewing Deep Learning-Based Feature Extractors in a Novel Automotive SLAM Framework","authors":"Christos Anagnostopoulos, A. Lalos, P. Kapsalas, Duong Nguyen Van, C. Stylios","doi":"10.1109/MED59994.2023.10185780","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185780","url":null,"abstract":"Simultaneous Localization and Mapping (SLAM), which is characterized as a core problem in autonomous vehicles, involves the estimation of the vehicle’s position and the concurrent building of the map of the environment. The use of deep learning-based feature extractors has gain increasing popularity since they possess the ability to extract reliable and repeatable features from raw sensor data. However, the performance of deep learning-based approaches varies depending on the application, environmental conditions, and the type of implemented technology. In this paper, we evaluate the performance of several deep learning-based feature extractors integrated into a SLAM system, using as input real and synthetic data, which implement common odometry problems. To our knowledge, this is the first work that benchmarks the accuracy of deep-learning based algorithms in estimating the vehicle’s trajectory in specific odometry corner cases.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"123 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":"116049489","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185804
Georgiana Cretu, Iulia Stamatescu, G. Stamatescu
Accurately estimating and forecasting building occupancy represents an important tasks for higher level indoor energy management and control routines. Extended availability of public and open datasets reflecting indoor conditions through various sensor measurement and indirect proxies of human activity enable reliable benchmarking of new techniques for pre-processing and learning of occupancy patterns. In this work we present a comparative study between deep learning, such as convolutional neural networks, and conventional machine learning approaches, such as decision trees and random forests, on an a reference occupancy dataset. The various design decision and parametrisation options are discussed. The building occupancy classification task involves generating model outputs for various discrete occupancy categories. Standardised metrics such as accuracy, precision, recall and the F1-score are used for replicable benchmarking of the results. Main finding of the study is that, though generally the deep learning methods offer better overall results, the addition of relevant features (sensors) to the input dataset can yield better results for the conventional machine learning models with significantly lower training time and model size. This results in suitable, fast-inference, models for embedded deployment in physical proximity to the process.
{"title":"Evaluation of Deep Learning and Machine Learning Algorithms for Building Occupancy Classification on Open Datasets","authors":"Georgiana Cretu, Iulia Stamatescu, G. Stamatescu","doi":"10.1109/MED59994.2023.10185804","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185804","url":null,"abstract":"Accurately estimating and forecasting building occupancy represents an important tasks for higher level indoor energy management and control routines. Extended availability of public and open datasets reflecting indoor conditions through various sensor measurement and indirect proxies of human activity enable reliable benchmarking of new techniques for pre-processing and learning of occupancy patterns. In this work we present a comparative study between deep learning, such as convolutional neural networks, and conventional machine learning approaches, such as decision trees and random forests, on an a reference occupancy dataset. The various design decision and parametrisation options are discussed. The building occupancy classification task involves generating model outputs for various discrete occupancy categories. Standardised metrics such as accuracy, precision, recall and the F1-score are used for replicable benchmarking of the results. Main finding of the study is that, though generally the deep learning methods offer better overall results, the addition of relevant features (sensors) to the input dataset can yield better results for the conventional machine learning models with significantly lower training time and model size. This results in suitable, fast-inference, models for embedded deployment in physical proximity to the process.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"21 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":"123483287","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185769
Andreas P. Kechagias, G. Rovithakis
In this work we consider uncertain impulsive systems in Brunovsky canonical form with possibly aperiodic impulses. Following the prescribed performance control methodology, a state feedback controller is designed to guarantee that between any two consecutive impulses, the output tracking error will converge to a neighborhood of zero of predefined size, in no greater than a user selected fixed-time. In addition, all signals in the closed-loop are bounded. Simulations clarify and verify the approach.
{"title":"Achieving Prescribed Performance for Uncertain Impulsive Systems in Brunovsky Canonical Form*","authors":"Andreas P. Kechagias, G. Rovithakis","doi":"10.1109/MED59994.2023.10185769","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185769","url":null,"abstract":"In this work we consider uncertain impulsive systems in Brunovsky canonical form with possibly aperiodic impulses. Following the prescribed performance control methodology, a state feedback controller is designed to guarantee that between any two consecutive impulses, the output tracking error will converge to a neighborhood of zero of predefined size, in no greater than a user selected fixed-time. In addition, all signals in the closed-loop are bounded. Simulations clarify and verify the approach.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"153 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":"123598954","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185785
L. Schmitt, D. Abel
Fuel cell systems are a viable alternative for energy conversion in stationary and mobile applications. Advanced control algorithms are the main levers to ensure safe operation in transients and increase the applicability of fuel cell systems in research and industry. This paper focuses on the control of the fuel cell air path and the net power output for a small-scale fuel cell system. For safe operation and durability even in transients, tight bounds on stoichiometry and compressor operation must be ensured at all times. To tackle this challenge, a data-based nonlinear model predictive controller is implemented and experimentally validated on a cathode path test bench with a real-time fuel cell stack simulation. Our results show accurate tracking, safe operation, and a reduction in settling time to new power reference set points of approximately 50% compared to a reference controller.
{"title":"Nonlinear MPC for Fuel Cell Air Path Control with Experimental Validation","authors":"L. Schmitt, D. Abel","doi":"10.1109/MED59994.2023.10185785","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185785","url":null,"abstract":"Fuel cell systems are a viable alternative for energy conversion in stationary and mobile applications. Advanced control algorithms are the main levers to ensure safe operation in transients and increase the applicability of fuel cell systems in research and industry. This paper focuses on the control of the fuel cell air path and the net power output for a small-scale fuel cell system. For safe operation and durability even in transients, tight bounds on stoichiometry and compressor operation must be ensured at all times. To tackle this challenge, a data-based nonlinear model predictive controller is implemented and experimentally validated on a cathode path test bench with a real-time fuel cell stack simulation. Our results show accurate tracking, safe operation, and a reduction in settling time to new power reference set points of approximately 50% compared to a reference controller.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"1 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":"125788901","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185670
Salama Makni, A. Hajjaji, M. Chaabane
This paper investigates the problem of state and actuator/sensor fault (ASF) estimation for nonlinear systems described by Takagi-Sugeno (T-S) fuzzy models subject to external disturbances. A robust adaptive observer (RAO) is designed to estimate the system state, sensor faults and actuator faults conjointly. For the convergence analysis of all estimation errors, a fuzzy Lyapunov functional candidate combined by free weighting matrices have been constructed to obtain more relaxed results. The design conditions, taking into account the $H_{infty} $ performance, are formulated in terms of Linear Matrix Inequalities (LMIs). Finally, a comparative study is presented to prove the superiority of the proposed method.
{"title":"Relaxed fault estimation conditions for fuzzy systems subject to time varying actuator and sensor faults","authors":"Salama Makni, A. Hajjaji, M. Chaabane","doi":"10.1109/MED59994.2023.10185670","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185670","url":null,"abstract":"This paper investigates the problem of state and actuator/sensor fault (ASF) estimation for nonlinear systems described by Takagi-Sugeno (T-S) fuzzy models subject to external disturbances. A robust adaptive observer (RAO) is designed to estimate the system state, sensor faults and actuator faults conjointly. For the convergence analysis of all estimation errors, a fuzzy Lyapunov functional candidate combined by free weighting matrices have been constructed to obtain more relaxed results. The design conditions, taking into account the $H_{infty} $ performance, are formulated in terms of Linear Matrix Inequalities (LMIs). Finally, a comparative study is presented to prove the superiority of the proposed method.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"23 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":"117089984","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185758
S. S. Rawikara, H. Alwi, C. Edwards
This paper presents a fault-tolerant control scheme for a scale model of a High-Altitude Long Endurance UAV. The aircraft considered in this paper is a scale model glider that has a similar configuration to typical HALE platforms. The proposed control system was designed using sliding mode and control allocation to handle actuator faults. To evaluate the performance of the system, simulations were conducted using a nonlinear fixed-aerodynamic model. The results are promising, since the control system was able to handle multiple actuator failure cases.
{"title":"Fault Tolerant Control Using Sliding Modes for Scale Model of a High Altitude Long Endurance Aircraft","authors":"S. S. Rawikara, H. Alwi, C. Edwards","doi":"10.1109/MED59994.2023.10185758","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185758","url":null,"abstract":"This paper presents a fault-tolerant control scheme for a scale model of a High-Altitude Long Endurance UAV. The aircraft considered in this paper is a scale model glider that has a similar configuration to typical HALE platforms. The proposed control system was designed using sliding mode and control allocation to handle actuator faults. To evaluate the performance of the system, simulations were conducted using a nonlinear fixed-aerodynamic model. The results are promising, since the control system was able to handle multiple actuator failure cases.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"10 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":"116711931","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}
Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185811
Lukas Schichler, Karin Festl, M. Stolz, D. Watzenig
Autonomous vehicles (AVs) will be part of everyday life in the near future. In order to accelerate this process, many subsystems need to be optimised and validated. One of the most important subsystem of AVs is the steering controller. It’s task is to keep the vehicle on track, which is the reason, why many steering controllers have been designed for a large variety of applications. However, the validation of such controllers is a labour-intensive task, which is why in this paper, an Artificial Intelligence (AI) is trained to find an edge case path that brings the steering controller to its limits. This path is a sufficient substitute for a large set of paths and enables fast validation of steering controllers. This contribution describes the development of a reinforcement learning (RL) based path planner using the PPO-Algorithm to train a so called agent. Comparing the resulting key feature maps shows that the agent adapts to each controllers characteristics during the learning process. The result is demonstrated for three different state of the art path tracking controllers. For each controller the agent finds a path that leads to the controllers failure within seconds.
{"title":"RL-based path planning for controller performance validation","authors":"Lukas Schichler, Karin Festl, M. Stolz, D. Watzenig","doi":"10.1109/MED59994.2023.10185811","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185811","url":null,"abstract":"Autonomous vehicles (AVs) will be part of everyday life in the near future. In order to accelerate this process, many subsystems need to be optimised and validated. One of the most important subsystem of AVs is the steering controller. It’s task is to keep the vehicle on track, which is the reason, why many steering controllers have been designed for a large variety of applications. However, the validation of such controllers is a labour-intensive task, which is why in this paper, an Artificial Intelligence (AI) is trained to find an edge case path that brings the steering controller to its limits. This path is a sufficient substitute for a large set of paths and enables fast validation of steering controllers. This contribution describes the development of a reinforcement learning (RL) based path planner using the PPO-Algorithm to train a so called agent. Comparing the resulting key feature maps shows that the agent adapts to each controllers characteristics during the learning process. The result is demonstrated for three different state of the art path tracking controllers. For each controller the agent finds a path that leads to the controllers failure within seconds.","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":"114574675","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}
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