Pub Date : 2024-08-26DOI: 10.1109/tcst.2024.3443703
Josef Matouš, Kristin Y. Pettersen, Damiano Varagnolo, Claudio Paliotta
{"title":"A Distributed NSB Algorithm for Formation Path Following","authors":"Josef Matouš, Kristin Y. Pettersen, Damiano Varagnolo, Claudio Paliotta","doi":"10.1109/tcst.2024.3443703","DOIUrl":"https://doi.org/10.1109/tcst.2024.3443703","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"5 1 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats [1] . Understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.
{"title":"Guest Editorial: Special section on Resilient Control of Cyber-Physical Power and Energy Systems","authors":"Veronica Adetola;Thomas Edgar;Sai Pushpak Nandanoori;Quanyan Zhu;Craig Rieger;Masoud Abbaszadeh","doi":"10.1109/TCST.2024.3403515","DOIUrl":"https://doi.org/10.1109/TCST.2024.3403515","url":null,"abstract":"Our power and energy systems are becoming more and more integrated and interconnected. The increasing integration of edge devices and dependence on cyber infrastructure provides both the potential for benefits and risks. The integration enables more dynamic and flexible control paradigms while at the same time increasing the cyberattack surface and uncertainty of behavior. Control methodology in this new world must be designed for resilience and must have the ability to withstand, react, and respond to both physical faults and cyber-induced threats \u0000<xref>[1]</xref>\u0000. Understanding system resilience under adverse conditions requires studying control performance and how cyber infrastructure can integrate with and support the overall resilience of the system.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 5","pages":"1688-1691"},"PeriodicalIF":4.9,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640172","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1109/tcst.2024.3440249
Boxi Jiang, Mohammad Reza Amini, Yingqian Liao, Kartik Naik, Joaquim R. R. A. Martins, Jing Sun
{"title":"Control Co-Design of Hydrokinetic Turbines Considering Dynamic–Hydrodynamic Coupling","authors":"Boxi Jiang, Mohammad Reza Amini, Yingqian Liao, Kartik Naik, Joaquim R. R. A. Martins, Jing Sun","doi":"10.1109/tcst.2024.3440249","DOIUrl":"https://doi.org/10.1109/tcst.2024.3440249","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"6 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Decentralized Voltage Control of Boost Converters in DC Microgrids: Feasibility Guarantees","authors":"Morteza Nazari Monfared, Yu Kawano, Michele Cucuzzella","doi":"10.1109/tcst.2024.3440228","DOIUrl":"https://doi.org/10.1109/tcst.2024.3440228","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"167 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/tcst.2024.3437163
Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer, Tong Duy Son
{"title":"Learning-Based NMPC Adaptation for Autonomous Driving Using Parallelized Digital Twin","authors":"Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer, Tong Duy Son","doi":"10.1109/tcst.2024.3437163","DOIUrl":"https://doi.org/10.1109/tcst.2024.3437163","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"8 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/tcst.2024.3437142
Haixu Yu, Xudong Zhao, Chunlin Chen
{"title":"Quantum-Inspired Reinforcement Learning for Quantum Control","authors":"Haixu Yu, Xudong Zhao, Chunlin Chen","doi":"10.1109/tcst.2024.3437142","DOIUrl":"https://doi.org/10.1109/tcst.2024.3437142","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"56 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-06DOI: 10.1109/TCST.2024.3433229
Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano
We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.
{"title":"Decentralized, Safe, Multiagent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning","authors":"Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano","doi":"10.1109/TCST.2024.3433229","DOIUrl":"10.1109/TCST.2024.3433229","url":null,"abstract":"We propose a decentralized, multiagent motion planner that guarantees the probabilistic safety of a team subject to stochastic uncertainty in the agent model and environment. Our scalable approach generates safe motion plans in real-time using off-the-shelf, single-agent reinforcement learning (RL) rendered safe using distributionally robust, convex optimization and buffered Voronoi cells. We guarantee the recursive feasibility of the mean trajectories and mitigate the conservativeness using a temporal discounting of safety. We show in simulation that our approach generates safe and high-performant trajectories as compared to existing approaches, and further validate these observations in physical experiments using drones.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2492-2499"},"PeriodicalIF":4.9,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141939880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-02DOI: 10.1109/tcst.2024.3433206
Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, Andreas A. Malikopoulos
{"title":"Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios","authors":"Viet-Anh Le, Behdad Chalaki, Filippos N. Tzortzoglou, Andreas A. Malikopoulos","doi":"10.1109/tcst.2024.3433206","DOIUrl":"https://doi.org/10.1109/tcst.2024.3433206","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"215 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1109/tcst.2024.3433228
Pietro Lorenzetti, Florian Reissner, George Weiss
{"title":"A Synchronverter-Based Magnitude Phase-Locked Loop","authors":"Pietro Lorenzetti, Florian Reissner, George Weiss","doi":"10.1109/tcst.2024.3433228","DOIUrl":"https://doi.org/10.1109/tcst.2024.3433228","url":null,"abstract":"","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"43 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1109/TCST.2024.3426300
Jorge Val Ledesma;Rafał Wisniewski;Carsten S. Kallesøe;Agisilaos Tsouvalas
Reinforcement learning (RL) is a widely used control technique that finds an optimal policy using the feedback of its actions. The search for the optimal policy requires that the system explores a broad region of the state space. This search puts at risk the safe operation, since some of the explored regions might be near the physical system limits. Implementing learning methods in industrial applications is limited because of its uncertain behavior when finding an optimal policy. This work proposes an RL control algorithm with a filter that supervises the safety of the exploration based on a nominal model. The performance of this safety filter is increased by modeling the uncertainty with a Gaussian process (GP) regression. This method is applied to the optimization of the management of a water distribution network (WDN) with an elevated reservoir; the management objectives are to regulate the tank filling while maintaining an adequate water turnover. The proposed method is validated in a laboratory setup that emulates the hydraulic features of a WDN.
{"title":"Water Age Control for Water Distribution Networks via Safe Reinforcement Learning","authors":"Jorge Val Ledesma;Rafał Wisniewski;Carsten S. Kallesøe;Agisilaos Tsouvalas","doi":"10.1109/TCST.2024.3426300","DOIUrl":"https://doi.org/10.1109/TCST.2024.3426300","url":null,"abstract":"Reinforcement learning (RL) is a widely used control technique that finds an optimal policy using the feedback of its actions. The search for the optimal policy requires that the system explores a broad region of the state space. This search puts at risk the safe operation, since some of the explored regions might be near the physical system limits. Implementing learning methods in industrial applications is limited because of its uncertain behavior when finding an optimal policy. This work proposes an RL control algorithm with a filter that supervises the safety of the exploration based on a nominal model. The performance of this safety filter is increased by modeling the uncertainty with a Gaussian process (GP) regression. This method is applied to the optimization of the management of a water distribution network (WDN) with an elevated reservoir; the management objectives are to regulate the tank filling while maintaining an adequate water turnover. The proposed method is validated in a laboratory setup that emulates the hydraulic features of a WDN.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2332-2343"},"PeriodicalIF":4.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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