When encountering the distribution shift between the source (training) and target (test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are well-labeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation: distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.
{"title":"Low-Rank Optimal Transport for Robust Domain Adaptation","authors":"Bingrong Xu;Jianhua Yin;Cheng Lian;Yixin Su;Zhigang Zeng","doi":"10.1109/JAS.2024.124344","DOIUrl":"https://doi.org/10.1109/JAS.2024.124344","url":null,"abstract":"When encountering the distribution shift between the source (training) and target (test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are well-labeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation: distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1667-1680"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter is concerned with the secure tracking control problem in the unmanned aerial vehicle (UAV) system by fixed-time convergent reinforcement learning (RL). By virtue of the zero-sum game, the false data injection (FDI) attacker and secure controller are viewed as game players. Then, the attack-defense process is recast as a min-max problem. For solving the problem and acquiring the optimal secure control policy, a single-critic RL algorithm with fixed-time convergence is presented. Meanwhile, the associated convergence and stability proofs are given. A simulation is provided to show the effectiveness of the raised method.
{"title":"Secure Tracking Control via Fixed-Time Convergent Reinforcement Learning for a UAV CPS","authors":"Zhenyu Gong;Feisheng Yang","doi":"10.1109/JAS.2023.124149","DOIUrl":"https://doi.org/10.1109/JAS.2023.124149","url":null,"abstract":"Dear Editor, This letter is concerned with the secure tracking control problem in the unmanned aerial vehicle (UAV) system by fixed-time convergent reinforcement learning (RL). By virtue of the zero-sum game, the false data injection (FDI) attacker and secure controller are viewed as game players. Then, the attack-defense process is recast as a min-max problem. For solving the problem and acquiring the optimal secure control policy, a single-critic RL algorithm with fixed-time convergence is presented. Meanwhile, the associated convergence and stability proofs are given. A simulation is provided to show the effectiveness of the raised method.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1699-1701"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}
In this paper, the optimal variational generalized Nash equilibrium (v-GNE) seeking problem in merely monotone games with linearly coupled cost functions is investigated, in which the feasible strategy domain of each agent is coupled through an affine constraint. A distributed algorithm based on the hybrid steepest descent method is first proposed to seek the optimal v-GNE. Then, an accelerated algorithm with relaxation is proposed and analyzed, which has the potential to further improve the convergence speed to the optimal v-GNE. Some sufficient conditions in both algorithms are obtained to ensure the global convergence towards the optimal v-GNE. To illustrate the performance of the algorithms, numerical simulation is conducted based on a networked Nash-Cournot game with bounded market capacities.
{"title":"Distributed Optimal Variational GNE Seeking in Merely Monotone Games","authors":"Wangli He;Yanzhen Wang","doi":"10.1109/JAS.2024.124284","DOIUrl":"https://doi.org/10.1109/JAS.2024.124284","url":null,"abstract":"In this paper, the optimal variational generalized Nash equilibrium (v-GNE) seeking problem in merely monotone games with linearly coupled cost functions is investigated, in which the feasible strategy domain of each agent is coupled through an affine constraint. A distributed algorithm based on the hybrid steepest descent method is first proposed to seek the optimal v-GNE. Then, an accelerated algorithm with relaxation is proposed and analyzed, which has the potential to further improve the convergence speed to the optimal v-GNE. Some sufficient conditions in both algorithms are obtained to ensure the global convergence towards the optimal v-GNE. To illustrate the performance of the algorithms, numerical simulation is conducted based on a networked Nash-Cournot game with bounded market capacities.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1621-1630"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a model predictive control (MPC) framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system. Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically, and is supported by simulation examples.
{"title":"Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control","authors":"Bing Zhu;Xiaozhuoer Yuan;Li Dai;Zhiwen Qiang","doi":"10.1109/JAS.2024.124212","DOIUrl":"https://doi.org/10.1109/JAS.2024.124212","url":null,"abstract":"In this paper, a model predictive control (MPC) framework is proposed for finite-time stabilization of linear and nonlinear discrete-time systems subject to state and control constraints. The proposed MPC framework guarantees the finite-time convergence property by assigning the control horizon equal to the dimension of the overall system, and only penalizing the terminal cost in the optimization, where the stage costs are not penalized explicitly. A terminal inequality constraint is added to guarantee the feasibility and stability of the closed-loop system. Initial feasibility can be improved via augmentation. The finite-time convergence of the proposed MPC is proved theoretically, and is supported by simulation examples.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1656-1666"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Set stabilization is one of the essential problems in engineering systems, and self-triggered control (STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed, respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.
{"title":"Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications","authors":"Rong Zhao;Jun-e Feng;Dawei Zhang","doi":"10.1109/JAS.2023.124050","DOIUrl":"https://doi.org/10.1109/JAS.2023.124050","url":null,"abstract":"Set stabilization is one of the essential problems in engineering systems, and self-triggered control (STC) can save the storage space for interactive information, and can be successfully applied in networked control systems with limited communication resources. In this study, the set stabilization problem and STC design of Boolean control networks are investigated via the semi-tensor product technique. On the one hand, the largest control invariant subset is calculated in terms of the strongly connected components of the state transition graph, by which a graph-theoretical condition for set stabilization is derived. On the other hand, a characteristic function is exploited to determine the triggering mechanism and feasible controls. Based on this, the minimum-time and minimum-triggering open-loop, state-feedback and output-feedback STCs for set stabilization are designed, respectively. As classic applications of self-triggered set stabilization, self-triggered synchronization, self-triggered output tracking and self-triggered output regulation are discussed as well. Additionally, several practical examples are given to illustrate the effectiveness of theoretical results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1631-1642"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dear Editor, This letter develops a novel method to implement event-triggered optimal control (ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning (DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions. Then, dueling double deep Q-network (D3QN) is utilized to achieve our method. In simulations, we present a preliminary comparative study of DRL and Lyapunov analysis for ETOC.
{"title":"Deep Reinforcement Learning or Lyapunov Analysis? A Preliminary Comparative Study on Event-Triggered Optimal Control","authors":"Jingwei Lu;Lefei Li;Qinglai Wei;Fei–Yue Wang","doi":"10.1109/JAS.2024.124434","DOIUrl":"https://doi.org/10.1109/JAS.2024.124434","url":null,"abstract":"Dear Editor, This letter develops a novel method to implement event-triggered optimal control (ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning (DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions. Then, dueling double deep Q-network (D3QN) is utilized to achieve our method. In simulations, we present a preliminary comparative study of DRL and Lyapunov analysis for ETOC.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1702-1704"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}
Shengnan Gao;Zhouhua Peng;Haoliang Wang;Lu Liu;Dan Wang
Dear Editor, This letter addresses long duration coverage problem of multiple robotic surface vehicles (RSVs) subject to battery energy constraints, in addition to uncertainties and disturbances. An anti-disturbance energy-aware control method is proposed for performing coverage task of RSVs. Firstly, a centroidal Voronoi tessellation (CVT) is used to optimize the partition of the given coverage area. The optimal position for each vehicle corresponds to the centroid of the Voronoi cell. Secondly, by consisting two battery energy control barrier functions, an energy-aware kinematic guidance law is designed to drive each RSV to the optimal position. Finally, an anti-disturbance fixed-time kinetic control law is designed for each RSV to track the desired speed based on the fixed-time extended state observer and nonlinear tracking differentiator. By the control method, RSVs are capable of achieving the long duration coverage within the given coverage area. Simulation results verify the effectiveness of the proposed anti-disturbance energy-aware control method for multi-RSV.
{"title":"Long Duration Coverage Control of Multiple Robotic Surface Vehicles Under Battery Energy Constraints","authors":"Shengnan Gao;Zhouhua Peng;Haoliang Wang;Lu Liu;Dan Wang","doi":"10.1109/JAS.2023.123438","DOIUrl":"https://doi.org/10.1109/JAS.2023.123438","url":null,"abstract":"Dear Editor, This letter addresses long duration coverage problem of multiple robotic surface vehicles (RSVs) subject to battery energy constraints, in addition to uncertainties and disturbances. An anti-disturbance energy-aware control method is proposed for performing coverage task of RSVs. Firstly, a centroidal Voronoi tessellation (CVT) is used to optimize the partition of the given coverage area. The optimal position for each vehicle corresponds to the centroid of the Voronoi cell. Secondly, by consisting two battery energy control barrier functions, an energy-aware kinematic guidance law is designed to drive each RSV to the optimal position. Finally, an anti-disturbance fixed-time kinetic control law is designed for each RSV to track the desired speed based on the fixed-time extended state observer and nonlinear tracking differentiator. By the control method, RSVs are capable of achieving the long duration coverage within the given coverage area. Simulation results verify the effectiveness of the proposed anti-disturbance energy-aware control method for multi-RSV.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1695-1698"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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}
For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore, we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.
{"title":"A LiDAR Point Clouds Dataset of Ships in a Maritime Environment","authors":"Qiuyu Zhang;Lipeng Wang;Hao Meng;Wen Zhang;Genghua Huang","doi":"10.1109/JAS.2024.124275","DOIUrl":"https://doi.org/10.1109/JAS.2024.124275","url":null,"abstract":"For the first time, this article introduces a LiDAR Point Clouds Dataset of Ships composed of both collected and simulated data to address the scarcity of LiDAR data in maritime applications. The collected data are acquired using specialized maritime LiDAR sensors in both inland waterways and wide-open ocean environments. The simulated data is generated by placing a ship in the LiDAR coordinate system and scanning it with a redeveloped Blensor that emulates the operation of a LiDAR sensor equipped with various laser beams. Furthermore, we also render point clouds for foggy and rainy weather conditions. To describe a realistic shipping environment, a dynamic tail wave is modeled by iterating the wave elevation of each point in a time series. Finally, networks serving small objects are migrated to ship applications by feeding our dataset. The positive effect of simulated data is described in object detection experiments, and the negative impact of tail waves as noise is verified in single-object tracking experiments. The Dataset is available at https://github.com/zqy411470859/ship_dataset.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"11 7","pages":"1681-1694"},"PeriodicalIF":11.8,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper revisits the problem of bumpless transfer control (BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control (IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent mode-dependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains (with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and $H_{infty}$