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IEEE Transactions on Control Systems Technology最新文献

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Decentralized Voltage Control of Boost Converters in DC Microgrids: Feasibility Guarantees 直流微电网中升压转换器的分散电压控制：可行性保证
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-15 DOI: 10.1109/TCST.2024.3440228
Morteza Nazari Monfared;Yu Kawano;Michele Cucuzzella
{"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":"10.1109/TCST.2024.3440228","url":null,"abstract":"This article deals with the design of a decentralized dynamic control scheme to regulate the voltage of a direct current (dc) microgrid composed of boost converters supplying unknown loads. Moreover, the proposed control scheme guarantees that physical system constraints are satisfied at each time instant. Specifically, we guarantee that the voltages evolve in the positive orthant and that the duty cycle of each boost converter remains within specified bounds. The control design is based on Lyapunov theory and, more precisely, we use a Krasovskii Lyapunov function to estimate a feasible domain of attraction of the closed-loop system. Then, we guarantee that for any initial condition inside the estimated domain of attraction, the desired equilibrium point is asymptotically stable and the physical constraints are satisfied at each time instant. Finally, we assess the effectiveness of the proposed control scheme through extensive and realistic simulation scenarios.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"3-15"},"PeriodicalIF":4.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637465","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Learning-Based NMPC Adaptation for Autonomous Driving Using Parallelized Digital Twin 利用并行化数字孪生系统为自动驾驶提供基于学习的 NMPC 适应性
IF 4.8 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-14 DOI: 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}
引用次数: 0
Quantum-Inspired Reinforcement Learning for Quantum Control 量子控制的量子强化学习
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-14 DOI: 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":"10.1109/TCST.2024.3437142","url":null,"abstract":"Reinforcement learning (RL) is considered a powerful technology with the potential to revolutionize quantum control. However, the application effectiveness of traditional RL is often limited by some insurmountable experimental conditions. Thus, developing new RL algorithms that can efficiently manipulate the quantum system dynamics is a crucial task. Prior research has shown that incorporating quantum mechanical properties into RL can improve learning performance. In this article, we consider the quantum control problem where only the target state can be accurately identified and introduce a quantum-inspired RL (QiRL) method. In particular, we propose a quantum-inspired exploration strategy to replace a commonly used \u0000<inline-formula> <tex-math>$epsilon $ </tex-math></inline-formula>\u0000-greedy strategy, as well as a quantum-inspired reward scheme to incentivize the learning agent. Numerical results on three quantum system control problems, i.e., one-qubit closed quantum system, two-level open quantum system, and many-qubit closed quantum system, verify the effectiveness of QiRL. Comparison results show that the proposed QiRL outperforms existing RL algorithms (deep Q-network and proximal policy optimization) in terms of stability and efficiency for solving quantum control problems.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"61-76"},"PeriodicalIF":4.9,"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}
引用次数: 0
Decentralized, Safe, Multiagent Motion Planning for Drones Under Uncertainty via Filtered Reinforcement Learning 通过过滤强化学习实现不确定性条件下无人机的分散、安全、多代理运动规划
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-06 DOI: 10.1109/TCST.2024.3433229
Abraham P. Vinod;Sleiman Safaoui;Tyler H. Summers;Nobuyuki Yoshikawa;Stefano Di Cairano
{"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}
引用次数: 0
Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios 混合交通并线场景中互联车辆和自动驾驶车辆的随机时间最优轨迹规划
IF 4.8 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-02 DOI: 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}
引用次数: 0
A Synchronverter-Based Magnitude Phase-Locked Loop 基于同步器的幅度锁相环
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-01 DOI: 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":"10.1109/TCST.2024.3433228","url":null,"abstract":"A magnitude phase-locked loop (MPLL) is a system that synchronizes its output signal in frequency, phase, and magnitude with the dominant sinusoidal component of its input signal. We propose a novel MPLL design based on the model of a synchronverter [i.e., an inverter that behaves toward the power grid like a synchronous generator (SG)]. The synchronverter model is detached from its usual three-phase power electronics environment and transformed into a (single phase) MPLL with a wide pull-in range and great noise rejection properties. We prove synchronization under reasonable conditions. Extensive simulation results are provided to validate its performance and to compare it with existing solutions.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"33 1","pages":"32-47"},"PeriodicalIF":4.9,"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}
引用次数: 0
Water Age Control for Water Distribution Networks via Safe Reinforcement Learning 通过安全强化学习实现输水管网的水龄控制
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-08-01 DOI: 10.1109/TCST.2024.3426300
Jorge Val Ledesma;Rafał Wisniewski;Carsten S. Kallesøe;Agisilaos Tsouvalas
{"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}
引用次数: 0
Safe Battery Control Using Cascade-Control-Barrier Functions 利用级联-控制-阻隔功能实现安全电池控制
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-07-30 DOI: 10.1109/TCST.2024.3430708
Shuang Feng;Ricardo de Castro;Iman Ebrahimi
{"title":"Safe Battery Control Using Cascade-Control-Barrier Functions","authors":"Shuang Feng;Ricardo de Castro;Iman Ebrahimi","doi":"10.1109/TCST.2024.3430708","DOIUrl":"10.1109/TCST.2024.3430708","url":null,"abstract":"This article proposes a control barrier function (CBF) approach for fast charging and discharging of batteries under temperature, state of charge (SoC), and terminal voltage constraints. To improve numerical efficiency, we derive a cascade CBF formulation, which divides this safety problem into multiple layers that are easier to formulate and implement. The proposed algorithm exhibits a computational speed that is seven times faster than the model predictive control (MPC) and 3.6 times faster than the traditional single-layer (central) CBF. In the charging scenario, experimental results indicate that the proposed algorithm reduces charging time by 20% in comparison to traditional constant current, constant voltage (CC-CV) methods without violating electro-thermal safety constraints. The discharging experiment illustrates that the cascade CBF effectively limits the battery’s performance to ensure compliance with safety constraints.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2344-2358"},"PeriodicalIF":4.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866698","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}
引用次数: 0
A Modular Framework for Task-Agnostic, Energy Shaping Control of Lower Limb Exoskeletons 下肢外骨骼的任务诊断、能量整形控制模块化框架
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-07-30 DOI: 10.1109/TCST.2024.3429908
Jianping Lin;Gray C. Thomas;Nikhil V. Divekar;Vamsi Peddinti;Robert D. Gregg
{"title":"A Modular Framework for Task-Agnostic, Energy Shaping Control of Lower Limb Exoskeletons","authors":"Jianping Lin;Gray C. Thomas;Nikhil V. Divekar;Vamsi Peddinti;Robert D. Gregg","doi":"10.1109/TCST.2024.3429908","DOIUrl":"10.1109/TCST.2024.3429908","url":null,"abstract":"Various backdrivable lower limb exoskeletons have demonstrated the electromechanical capability to assist volitional motions of able-bodied users and people with mild to moderate gait disorders, but there does not exist a control framework that can be deployed on any joint(s) to assist any activity of daily life in a provably stable manner. This article presents the modular, multitask optimal energy shaping (M-TOES) framework, which uses a convex, data-driven optimization to train an analytical control model to instantaneously determine assistive joint torques across activities for any lower limb exoskeleton joint configuration. The presented modular energy basis is sufficiently descriptive to fit normative human joint torques (given normative feedback from signals available to a given joint configuration) across sit-stand transitions, stair ascent/descent, ramp ascent/descent, and level walking at different speeds. We evaluated controllers for four joint configurations (unilateral/bilateral and hip/knee) of the modular backdrivable lower limb unloading exoskeleton (M-BLUE) exoskeleton on eight able-bodied users navigating a multiactivity circuit. The two unilateral conditions significantly lowered overall muscle activation across all tasks and subjects (p\u0000<inline-formula> <tex-math>$mathbf {lt }$ </tex-math></inline-formula>\u00000.001). In contrast, bilateral configurations had a minimal impact, possibly attributable to device weight and physical constraints.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2359-2375"},"PeriodicalIF":4.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866700","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}
引用次数: 0
Mixed Reality Environment and High-Dimensional Continuification Control for Swarm Robotics 混合现实环境与蜂群机器人的高维连续化控制
IF 4.9 2区 计算机科学
IEEE Transactions on Control Systems Technology Pub Date : 2024-07-30 DOI: 10.1109/TCST.2024.3430128
Gian Carlo Maffettone;Lorenzo Liguori;Eduardo Palermo;Mario Di Bernardo;Maurizio Porfiri
{"title":"Mixed Reality Environment and High-Dimensional Continuification Control for Swarm Robotics","authors":"Gian Carlo Maffettone;Lorenzo Liguori;Eduardo Palermo;Mario Di Bernardo;Maurizio Porfiri","doi":"10.1109/TCST.2024.3430128","DOIUrl":"10.1109/TCST.2024.3430128","url":null,"abstract":"Many new methodologies for the control of large-scale multiagent systems are based on macroscopic representations of the system dynamics, in the form of continuum approximations of large ensembles. These techniques, developed in the limit case of an infinite number of agents, are usually validated only through numerical simulations. Here, we introduce a mixed reality setup for testing swarm robotics techniques, focusing on the macroscopic collective motion of robotic swarms. This hybrid apparatus combines real differential drive robots and virtual agents to create a heterogeneous swarm of tunable size. We also extend continuification-based control methods for swarms to higher dimensions and experimentally assess their validity in the new platform. Our study demonstrates the effectiveness of the platform for conducting large-scale swarm robotics experiments, and it contributes new theoretical insights into control algorithms exploiting continuification approaches.","PeriodicalId":13103,"journal":{"name":"IEEE Transactions on Control Systems Technology","volume":"32 6","pages":"2484-2491"},"PeriodicalIF":4.9,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10614946","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866699","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}
引用次数: 0
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