Anusha Srikanthan, Aren Karapetyan, Vijay Kumar, Nikolai Matni
Many practical applications of optimal control are subject to real-time computational constraints. When applying model predictive control (MPC) in these settings, respecting timing constraints is achieved by limiting the number of iterations of the optimization algorithm used to compute control actions at each time step, resulting in so-called suboptimal MPC. This paper proposes a suboptimal MPC scheme based on the alternating direction method of multipliers (ADMM). With a focus on the linear quadratic regulator problem with state and input constraints, we show how ADMM can be used to split the MPC problem into iterative updates of an unconstrained optimal control problem (with an analytical solution), and a dynamics-free feasibility step. We show that using a warm-start approach combined with enough iterations per time-step, yields an ADMM-based suboptimal MPC scheme which asymptotically stabilizes the system and maintains recursive feasibility.
{"title":"Closed-loop Analysis of ADMM-based Suboptimal Linear Model Predictive Control","authors":"Anusha Srikanthan, Aren Karapetyan, Vijay Kumar, Nikolai Matni","doi":"arxiv-2409.11351","DOIUrl":"https://doi.org/arxiv-2409.11351","url":null,"abstract":"Many practical applications of optimal control are subject to real-time\u0000computational constraints. When applying model predictive control (MPC) in\u0000these settings, respecting timing constraints is achieved by limiting the\u0000number of iterations of the optimization algorithm used to compute control\u0000actions at each time step, resulting in so-called suboptimal MPC. This paper\u0000proposes a suboptimal MPC scheme based on the alternating direction method of\u0000multipliers (ADMM). With a focus on the linear quadratic regulator problem with\u0000state and input constraints, we show how ADMM can be used to split the MPC\u0000problem into iterative updates of an unconstrained optimal control problem\u0000(with an analytical solution), and a dynamics-free feasibility step. We show\u0000that using a warm-start approach combined with enough iterations per time-step,\u0000yields an ADMM-based suboptimal MPC scheme which asymptotically stabilizes the\u0000system and maintains recursive feasibility.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264329","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}
Koopman operator theory has proven to be highly significant in system identification, even for challenging scenarios involving nonlinear time-varying systems (NTVS). In this context, we examine a network of connected agents, each with limited observation capabilities, aiming to estimate the dynamics of an NTVS collaboratively. Drawing inspiration from Koopman operator theory, deep neural networks, and distributed consensus, we introduce a distributed algorithm for deep Koopman learning of the dynamics of an NTVS. This approach enables individual agents to approximate the entire dynamics despite having access to only partial state observations. We guarantee consensus not only on the estimated dynamics but also on its structure, i.e., the matrices encountered in the linear equation of the lifted Koopman system. We provide theoretical insights into the convergence of the learning process and accompanying numerical simulations.
{"title":"Distributed Deep Koopman Learning for Nonlinear Dynamics","authors":"Wenjian Hao, Lili Wang, Ayush Rai, Shaoshuai Mou","doi":"arxiv-2409.11586","DOIUrl":"https://doi.org/arxiv-2409.11586","url":null,"abstract":"Koopman operator theory has proven to be highly significant in system\u0000identification, even for challenging scenarios involving nonlinear time-varying\u0000systems (NTVS). In this context, we examine a network of connected agents, each\u0000with limited observation capabilities, aiming to estimate the dynamics of an\u0000NTVS collaboratively. Drawing inspiration from Koopman operator theory, deep\u0000neural networks, and distributed consensus, we introduce a distributed\u0000algorithm for deep Koopman learning of the dynamics of an NTVS. This approach\u0000enables individual agents to approximate the entire dynamics despite having\u0000access to only partial state observations. We guarantee consensus not only on\u0000the estimated dynamics but also on its structure, i.e., the matrices\u0000encountered in the linear equation of the lifted Koopman system. We provide\u0000theoretical insights into the convergence of the learning process and\u0000accompanying numerical simulations.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264251","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}
This work focuses on analyzing the vulnerability of unmanned aerial vehicles (UAVs) to stealthy black-box false data injection attacks on GPS measurements. We assume that the quadcopter is equipped with IMU and GPS sensors, and an arbitrary sensor fusion and controller are used to estimate and regulate the system's states, respectively. We consider the notion of stealthiness in the most general form, where the attack is defined to be stealthy if it cannot be detected by any existing anomaly detector. Then, we show that if the closed-loop control system is incrementally exponentially stable, the attacker can cause arbitrarily large deviation in the position trajectory by compromising only the GPS measurements. We also show that to conduct such stealthy impactfull attack values, the attacker does not need to have access to the model of the system. Finally, we illustrate our results in a UAV case study.
{"title":"Black-box Stealthy GPS Attacks on Unmanned Aerial Vehicles","authors":"Amir Khazraei, Haocheng Meng, Miroslav Pajic","doi":"arxiv-2409.11405","DOIUrl":"https://doi.org/arxiv-2409.11405","url":null,"abstract":"This work focuses on analyzing the vulnerability of unmanned aerial vehicles\u0000(UAVs) to stealthy black-box false data injection attacks on GPS measurements.\u0000We assume that the quadcopter is equipped with IMU and GPS sensors, and an\u0000arbitrary sensor fusion and controller are used to estimate and regulate the\u0000system's states, respectively. We consider the notion of stealthiness in the\u0000most general form, where the attack is defined to be stealthy if it cannot be\u0000detected by any existing anomaly detector. Then, we show that if the\u0000closed-loop control system is incrementally exponentially stable, the attacker\u0000can cause arbitrarily large deviation in the position trajectory by\u0000compromising only the GPS measurements. We also show that to conduct such\u0000stealthy impactfull attack values, the attacker does not need to have access to\u0000the model of the system. Finally, we illustrate our results in a UAV case\u0000study.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264311","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}
Large-scale next-generation networked systems like smart grids and vehicular networks facilitate extensive automation and autonomy through real-time communication of sensitive messages. Digital signatures are vital for such applications since they offer scalable broadcast authentication with non-repudiation. Yet, even conventional secure signatures (e.g., ECDSA, RSA) introduce significant cryptographic delays that can disrupt the safety of such delay-aware systems. With the rise of quantum computers breaking conventional intractability problems, these traditional cryptosystems must be replaced with post-quantum (PQ) secure ones. However, PQ-secure signatures are significantly costlier than their conventional counterparts, vastly exacerbating delay hurdles for real-time applications. We propose a new signature called Time Valid Probabilistic Data Structure HORS (TVPD-HORS) that achieves significantly lower end-to-end delay with a tunable PQ-security for real-time applications. We harness special probabilistic data structures as an efficient one-way function at the heart of our novelty, thereby vastly fastening HORS as a primitive for NIST PQ cryptography standards. TVPD-HORS permits tunable and fast processing for varying input sizes via One-hash Bloom Filter, excelling in time valid cases, wherein authentication with shorter security parameters is used for short-lived yet safety-critical messages. We show that TVPD-HORS verification is 2.7x and 5x faster than HORS in high-security and time valid settings, respectively. TVPD-HORS key generation is also faster, with a similar signing speed to HORS. Moreover, TVPD-HORS can increase the speed of HORS variants over a magnitude of time. These features make TVPD-HORS an ideal primitive to raise high-speed time valid versions of PQ-safe standards like XMSS and SPHINCS+, paving the way for real-time authentication of next-generation networks.
{"title":"Fast and Post-Quantum Authentication for Real-time Next Generation Networks with Bloom Filter","authors":"Kiarash Sedghighadikolaei, Attila A Yavuz","doi":"arxiv-2409.10813","DOIUrl":"https://doi.org/arxiv-2409.10813","url":null,"abstract":"Large-scale next-generation networked systems like smart grids and vehicular\u0000networks facilitate extensive automation and autonomy through real-time\u0000communication of sensitive messages. Digital signatures are vital for such\u0000applications since they offer scalable broadcast authentication with\u0000non-repudiation. Yet, even conventional secure signatures (e.g., ECDSA, RSA)\u0000introduce significant cryptographic delays that can disrupt the safety of such\u0000delay-aware systems. With the rise of quantum computers breaking conventional\u0000intractability problems, these traditional cryptosystems must be replaced with\u0000post-quantum (PQ) secure ones. However, PQ-secure signatures are significantly\u0000costlier than their conventional counterparts, vastly exacerbating delay\u0000hurdles for real-time applications. We propose a new signature called Time Valid Probabilistic Data Structure\u0000HORS (TVPD-HORS) that achieves significantly lower end-to-end delay with a\u0000tunable PQ-security for real-time applications. We harness special\u0000probabilistic data structures as an efficient one-way function at the heart of\u0000our novelty, thereby vastly fastening HORS as a primitive for NIST PQ\u0000cryptography standards. TVPD-HORS permits tunable and fast processing for\u0000varying input sizes via One-hash Bloom Filter, excelling in time valid cases,\u0000wherein authentication with shorter security parameters is used for short-lived\u0000yet safety-critical messages. We show that TVPD-HORS verification is 2.7x and\u00005x faster than HORS in high-security and time valid settings, respectively.\u0000TVPD-HORS key generation is also faster, with a similar signing speed to HORS.\u0000Moreover, TVPD-HORS can increase the speed of HORS variants over a magnitude of\u0000time. These features make TVPD-HORS an ideal primitive to raise high-speed time\u0000valid versions of PQ-safe standards like XMSS and SPHINCS+, paving the way for\u0000real-time authentication of next-generation networks.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264034","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}
Accurate estimation of noise parameters is critical for optimal filter performance, especially in systems where true noise parameter values are unknown or time-varying. This article presents a quaternion left-invariant extended Kalman filter (LI-EKF) for attitude estimation, integrated with an adaptive noise covariance estimation algorithm. By employing an iterative expectation-maximization (EM) approach, the filter can effectively estimate both process and measurement noise covariances. Extensive simulations demonstrate the superiority of the proposed method in terms of attitude estimation accuracy and robustness to initial parameter misspecification. The adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes it a promising solution for various applications requiring reliable attitude estimation, such as aerospace, robotics, and autonomous systems.
{"title":"Robust Attitude Estimation with Quaternion Left-Invariant EKF and Noise Covariance Tuning","authors":"Yash Pandey, Rahul Bhattacharyya, Yatindra Nath Singh","doi":"arxiv-2409.11496","DOIUrl":"https://doi.org/arxiv-2409.11496","url":null,"abstract":"Accurate estimation of noise parameters is critical for optimal filter\u0000performance, especially in systems where true noise parameter values are\u0000unknown or time-varying. This article presents a quaternion left-invariant\u0000extended Kalman filter (LI-EKF) for attitude estimation, integrated with an\u0000adaptive noise covariance estimation algorithm. By employing an iterative\u0000expectation-maximization (EM) approach, the filter can effectively estimate\u0000both process and measurement noise covariances. Extensive simulations\u0000demonstrate the superiority of the proposed method in terms of attitude\u0000estimation accuracy and robustness to initial parameter misspecification. The\u0000adaptive LI-EKF's ability to adapt to time-varying noise characteristics makes\u0000it a promising solution for various applications requiring reliable attitude\u0000estimation, such as aerospace, robotics, and autonomous systems.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264201","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}
This paper deals with the target localisation problem in search and rescue scenarios in which the technology is based on electromagnetic transceivers. The noise floor and the shape of the electromagnetic radiation pattern make this problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces with the inverse of the distance from the electromagnetic source thus impacting estimation-based techniques applicability. On the other hand, non-isotropic radiation patterns lessen the efficacy of gradient-based policies. In this work, we manage a fleet of autonomous agents, equipped with electromagnetic sensors, by combining gradient-based and estimation-based techniques to speed up the transmitter localisation. Simulations specialized in the ARTVA technology used in search and rescue in avalanche scenarios confirm that our scheme outperforms current solutions.
{"title":"Bearing-based Target Localisation in Search and Rescue Scenarios","authors":"Giulia Michieletto, Nicola Mimmo, Roberto Naldi, Angelo Cenedese","doi":"arxiv-2409.11221","DOIUrl":"https://doi.org/arxiv-2409.11221","url":null,"abstract":"This paper deals with the target localisation problem in search and rescue\u0000scenarios in which the technology is based on electromagnetic transceivers. The\u0000noise floor and the shape of the electromagnetic radiation pattern make this\u0000problem challenging. Indeed, on the one hand, the signal-to-noise ratio reduces\u0000with the inverse of the distance from the electromagnetic source thus impacting\u0000estimation-based techniques applicability. On the other hand, non-isotropic\u0000radiation patterns lessen the efficacy of gradient-based policies. In this\u0000work, we manage a fleet of autonomous agents, equipped with electromagnetic\u0000sensors, by combining gradient-based and estimation-based techniques to speed\u0000up the transmitter localisation. Simulations specialized in the ARTVA\u0000technology used in search and rescue in avalanche scenarios confirm that our\u0000scheme outperforms current solutions.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264314","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}
Model-free algorithms are brought into the control system's research with the emergence of reinforcement learning algorithms. However, there are two practical challenges of reinforcement learning-based methods. First, learning by interacting with the environment is highly complex. Second, constraints on the states (boundary conditions) require additional care since the state trajectory is implicitly defined from the inputs and system dynamics. To address these problems, this paper proposes a new model-free algorithm based on basis functions, gradient estimation, and the Lagrange method. The favorable performance of the proposed algorithm is shown using several examples under state-dependent switches and time delays.
{"title":"A Model-Free Optimal Control Method With Fixed Terminal States and Delay","authors":"Mi Zhou, Erik Verriest, Chaouki Abdallah","doi":"arxiv-2409.10722","DOIUrl":"https://doi.org/arxiv-2409.10722","url":null,"abstract":"Model-free algorithms are brought into the control system's research with the\u0000emergence of reinforcement learning algorithms. However, there are two\u0000practical challenges of reinforcement learning-based methods. First, learning\u0000by interacting with the environment is highly complex. Second, constraints on\u0000the states (boundary conditions) require additional care since the state\u0000trajectory is implicitly defined from the inputs and system dynamics. To\u0000address these problems, this paper proposes a new model-free algorithm based on\u0000basis functions, gradient estimation, and the Lagrange method. The favorable\u0000performance of the proposed algorithm is shown using several examples under\u0000state-dependent switches and time delays.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264327","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}
The software architecture behind modern autonomous vehicles (AV) is becoming more complex steadily. Safety verification is now an imminent task prior to the large-scale deployment of such convoluted models. For safety-critical tasks in navigation, it becomes imperative to perform a verification procedure on the trajectories proposed by the planning algorithm prior to deployment. Signal Temporal Logic (STL) constraints can dictate the safety requirements for an AV. A combination of STL constraints is called a specification. A key difference between STL and other logic constraints is that STL allows us to work on continuous signals. We verify the satisfaction of the STL specifications by calculating the robustness value for each signal within the specification. Higher robustness values indicate a safer system. Model Predictive Control (MPC) is one of the most widely used methods to control the navigation of an AV, with an underlying set of state and input constraints. Our research aims to formulate and test an MPC controller, with STL specifications as constraints, that can safely navigate an AV. The primary goal of the cost function is to minimize the control inputs. STL constraints will act as an additional layer of constraints that would change based on the scenario and task on hand. We propose using sTaliro, a MATLAB-based robustness calculator for STL specifications, formulated in a receding horizon control fashion for an AV navigation task. It inputs a simplified AV state space model and a set of STL specifications, for which it constructs a closed-loop controller. We test out our controller for different test cases/scenarios and verify the safe navigation of our AV model.
现代自动驾驶汽车（AV）背后的软件架构正变得越来越复杂。在大规模部署此类复杂模型之前，安全验证是一项迫在眉睫的任务。对于导航中的安全关键任务，在部署前对规划算法提出的轨迹执行验证程序已成为当务之急。信号时态逻辑（STL）约束可以决定导航设备的安全要求。STL 与其他逻辑约束的主要区别在于，STL 允许我们处理连续信号。我们通过计算规范中每个信号的鲁棒性值来验证 STL 规范是否满足要求。模型预测控制（MPC）是最广泛使用的控制飞行器导航的方法之一，其基础是一组状态和输入约束条件。我们的研究旨在制定和测试一种以 STL 规范为约束条件的 MPC 控制器，使其能够安全地为无人机导航。成本函数的主要目标是使控制输入最小化。STL 约束将作为额外的约束层，会根据手头的场景和任务发生变化。我们建议使用 sTaliro，这是一款基于 MATLAB 的 STL 规范鲁棒性计算器，它以后退地平线控制方式制定 AV 导航任务。它输入一个简化的 AV 状态空间模型和一组 STL 规格，并为其构建一个闭环控制器。我们针对不同的测试案例/场景对控制器进行了测试，并验证了 AV 模型的安全导航性能。
{"title":"Safety Verification and Navigation for Autonomous Vehicles based on Signal Temporal Logic Constraints","authors":"Aditya Parameshwaran, Yue Wang","doi":"arxiv-2409.10689","DOIUrl":"https://doi.org/arxiv-2409.10689","url":null,"abstract":"The software architecture behind modern autonomous vehicles (AV) is becoming\u0000more complex steadily. Safety verification is now an imminent task prior to the\u0000large-scale deployment of such convoluted models. For safety-critical tasks in\u0000navigation, it becomes imperative to perform a verification procedure on the\u0000trajectories proposed by the planning algorithm prior to deployment. Signal\u0000Temporal Logic (STL) constraints can dictate the safety requirements for an AV.\u0000A combination of STL constraints is called a specification. A key difference\u0000between STL and other logic constraints is that STL allows us to work on\u0000continuous signals. We verify the satisfaction of the STL specifications by\u0000calculating the robustness value for each signal within the specification.\u0000Higher robustness values indicate a safer system. Model Predictive Control\u0000(MPC) is one of the most widely used methods to control the navigation of an\u0000AV, with an underlying set of state and input constraints. Our research aims to\u0000formulate and test an MPC controller, with STL specifications as constraints,\u0000that can safely navigate an AV. The primary goal of the cost function is to\u0000minimize the control inputs. STL constraints will act as an additional layer of\u0000constraints that would change based on the scenario and task on hand. We\u0000propose using sTaliro, a MATLAB-based robustness calculator for STL\u0000specifications, formulated in a receding horizon control fashion for an AV\u0000navigation task. It inputs a simplified AV state space model and a set of STL\u0000specifications, for which it constructs a closed-loop controller. We test out\u0000our controller for different test cases/scenarios and verify the safe\u0000navigation of our AV model.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264032","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}
Dennis Bank, Simon F. G. Ehlers, Karl-Philipp Kortmann, Tobias Zeller, Patrick Cujic, Thomas Seel
Refrigerated truck trailers are currently mainly operated with environmentally harmful diesel units; an alternative is to operate the refrigeration unit with electrical energy. However, this requires a battery, the size of which can be reduced by using a recuperation axle, which recovers energy during braking. Current systems work purely reactively and often in so-called towing mode, in which a generator torque is provided without a braking request from the driver in order to secure the energy supply. However, this drag leads to additional consumption in the truck. This work quantifies the potential of predictive energy management that uses route and environmental data to minimize CO2 emissions. This was done using simulation data obtained with the help of VECTO. It was shown that there is still considerable potential for savings, so this paper provides an important basis for the later development of predictive energy management and, thus, for the electrification of refrigerated truck transports.
{"title":"Predictive Energy Management for Recuperation Axles in Refrigerated Trailers","authors":"Dennis Bank, Simon F. G. Ehlers, Karl-Philipp Kortmann, Tobias Zeller, Patrick Cujic, Thomas Seel","doi":"arxiv-2409.10414","DOIUrl":"https://doi.org/arxiv-2409.10414","url":null,"abstract":"Refrigerated truck trailers are currently mainly operated with\u0000environmentally harmful diesel units; an alternative is to operate the\u0000refrigeration unit with electrical energy. However, this requires a battery,\u0000the size of which can be reduced by using a recuperation axle, which recovers\u0000energy during braking. Current systems work purely reactively and often in\u0000so-called towing mode, in which a generator torque is provided without a\u0000braking request from the driver in order to secure the energy supply. However,\u0000this drag leads to additional consumption in the truck. This work quantifies\u0000the potential of predictive energy management that uses route and environmental\u0000data to minimize CO2 emissions. This was done using simulation data obtained\u0000with the help of VECTO. It was shown that there is still considerable potential\u0000for savings, so this paper provides an important basis for the later\u0000development of predictive energy management and, thus, for the electrification\u0000of refrigerated truck transports.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264105","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}
Quang-Ha Ngo, Isabel Barnola, Tuyen Vu, Jianhua Zhang, Harsha Ravindra, Karl Schoder, Herbert Ginn
The integration of power electronics building blocks in modern MVDC 12kV Naval ship systems enhances energy management and functionality but also introduces complex fault detection and control challenges. These challenges strain traditional fault diagnostic methods, making it difficult to detect and manage faults across multiple locations while maintaining system stability and performance. This paper proposes a temporal recurrent graph transformer network for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural network uses gated recurrent units to capture temporal features and a multi-head attention mechanism to extract spatial features, enhancing diagnostic accuracy. The approach effectively identifies and evaluates successive multiple faults with high precision. The method is implemented and validated on the MVDC 12kV shipboard system designed by the ESDRC team, incorporating all key components. Results show significant improvements in fault localization accuracy, with a 1-4% increase in performance metrics compared to other machine learning methods.
{"title":"Recurrent Graph Transformer Network for Multiple Fault Localization in Naval Shipboard Systems","authors":"Quang-Ha Ngo, Isabel Barnola, Tuyen Vu, Jianhua Zhang, Harsha Ravindra, Karl Schoder, Herbert Ginn","doi":"arxiv-2409.10792","DOIUrl":"https://doi.org/arxiv-2409.10792","url":null,"abstract":"The integration of power electronics building blocks in modern MVDC 12kV\u0000Naval ship systems enhances energy management and functionality but also\u0000introduces complex fault detection and control challenges. These challenges\u0000strain traditional fault diagnostic methods, making it difficult to detect and\u0000manage faults across multiple locations while maintaining system stability and\u0000performance. This paper proposes a temporal recurrent graph transformer network\u0000for fault diagnosis in naval MVDC 12kV shipboard systems. The deep graph neural\u0000network uses gated recurrent units to capture temporal features and a\u0000multi-head attention mechanism to extract spatial features, enhancing\u0000diagnostic accuracy. The approach effectively identifies and evaluates\u0000successive multiple faults with high precision. The method is implemented and\u0000validated on the MVDC 12kV shipboard system designed by the ESDRC team,\u0000incorporating all key components. Results show significant improvements in\u0000fault localization accuracy, with a 1-4% increase in performance metrics\u0000compared to other machine learning methods.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142264326","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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