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Low-Rank Optimal Transport for Robust Domain Adaptation 稳健领域适应的低库最优传输
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124344
Bingrong Xu;Jianhua Yin;Cheng Lian;Yixin Su;Zhigang Zeng
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.
当遇到源域(训练域)和目标域(测试域)之间的分布变化时,域自适应试图调整分类器,使其能够处理不同的域。以前的领域适应研究在理论和实践上都取得了很大的成功,前提是源领域中的所有示例都是标记良好且质量较高的。然而,在源域数据的标签或特征被破坏的高噪声环境中,这些方法始终会失去鲁棒性,而这在现实中是很常见的。因此,人们引入了鲁棒域自适应来解决此类问题。在本文中,我们试图用稳健域适应解决两个相互关联的问题:跨域分布偏移和源域的样本噪声。为了解决这些难题,我们采用了一种带有低等级约束的最优传输方法来指导域适应模型的训练过程,以避免噪声信息的影响。对于域转移问题,最优传输机制可以利用差异测量来学习源域和目标域之间的联合数据表示,并保留判别信息。在处理损坏的源数据时,传输矩阵上的秩约束有助于恢复损坏的子空间结构,并在一定程度上消除噪声。这个松弛和正则化的最优传输框架是一个凸优化问题,可以用增强拉格朗日乘法求解,其收敛性可以用数学方法证明。通过在合成数据集和实际数据集上进行大量实验,对所提方法的有效性进行了评估。
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引用次数: 0
Secure Tracking Control via Fixed-Time Convergent Reinforcement Learning for a UAV CPS 通过固定时间聚合强化学习实现无人机 CPS 的安全跟踪控制
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.124149
Zhenyu Gong;Feisheng Yang
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.
亲爱的编辑,这封信关注的是通过固定时间收敛强化学习(RL)解决无人飞行器(UAV)系统中的安全跟踪控制问题。通过零和博弈,将虚假数据注入(FDI)攻击者和安全控制器视为博弈者。然后,攻击-防御过程被重构为一个最小-最大问题。为了解决这个问题并获得最佳安全控制策略,提出了一种具有固定时间收敛性的单批判 RL 算法。同时,给出了相关的收敛性和稳定性证明。还提供了一个仿真来展示所提出方法的有效性。
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引用次数: 0
Distributed Optimal Variational GNE Seeking in Merely Monotone Games 单纯单调博弈中的分布式最优变式 GNE 寻求
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124284
Wangli He;Yanzhen Wang
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.
本文研究了具有线性耦合成本函数的单纯单调博弈中的最优变分广义纳什均衡(v-GNE)寻求问题,其中每个代理的可行策略域通过仿射约束耦合。首先提出了一种基于混合最陡下降法的分布式算法来寻求最优 v-GNE。然后,提出并分析了一种带松弛的加速算法,该算法有可能进一步提高对最优 v-GNE 的收敛速度。在这两种算法中,都得到了一些充分条件,以确保向最优 v-GNE 的全局收敛。为了说明这两种算法的性能,基于市场容量有界的网络纳什-库诺博弈进行了数值模拟。
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引用次数: 0
Finite-Time Stabilization for Constrained Discrete-time Systems by Using Model Predictive Control 利用模型预测控制实现受限离散时间系统的有限时间稳定
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124212
Bing Zhu;Xiaozhuoer Yuan;Li Dai;Zhiwen Qiang
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.
本文提出了一种模型预测控制(MPC)框架,用于受状态和控制约束的线性和非线性离散时间系统的有限时间稳定。所提出的 MPC 框架通过分配与整个系统维度相等的控制范围,并在优化过程中只惩罚终端成本,而不明确惩罚阶段成本,从而保证了有限时间收敛特性。为保证闭环系统的可行性和稳定性,还添加了终端不等式约束。初始可行性可以通过增强来改善。本文从理论上证明了所提出的 MPC 的有限时间收敛性,并通过仿真实例予以支持。
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引用次数: 0
Self-Triggered Set Stabilization of Boolean Control Networks and Its Applications 布尔控制网络的自触发集稳定及其应用
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.124050
Rong Zhao;Jun-e Feng;Dawei Zhang
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.
集合稳定是工程系统中的基本问题之一,而自触发控制(STC)可以节省交互信息的存储空间,并能在通信资源有限的网络化控制系统中成功应用。本研究通过半张量积技术研究了布尔控制网络的集稳定问题和 STC 设计。一方面,根据状态转换图中的强连接成分计算出最大控制不变子集,从而推导出集合稳定的图论条件。另一方面,利用特征函数确定触发机制和可行控制。在此基础上,分别设计了用于机组稳定的最小时间和最小触发开环、状态反馈和输出反馈 STC。作为自触发机组稳定的经典应用,还讨论了自触发同步、自触发输出跟踪和自触发输出调节。此外,还给出了几个实际例子来说明理论结果的有效性。
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引用次数: 0
Deep Reinforcement Learning or Lyapunov Analysis? A Preliminary Comparative Study on Event-Triggered Optimal Control 深度强化学习还是李亚普诺夫分析?事件触发优化控制的初步比较研究
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124434
Jingwei Lu;Lefei Li;Qinglai Wei;Fei–Yue Wang
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.
亲爱的编辑,这封信提出了一种利用并行控制和深度强化学习(DRL)实现离散时间非线性系统事件触发最优控制(ETOC)的新方法,简称为 Deep-ETOC。所开发的深度-ETOC方法通过并行控制将通信成本引入性能指标,因此所开发的方法可以使控制系统在没有触发条件的情况下直接学习ETOC策略。然后,我们利用决斗双深 Q 网络(D3QN)来实现我们的方法。在仿真中,我们对 ETOC 的 DRL 和 Lyapunov 分析进行了初步比较研究。
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引用次数: 0
Long Duration Coverage Control of Multiple Robotic Surface Vehicles Under Battery Energy Constraints 电池能量限制下多个机器人水面飞行器的长时间覆盖控制
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.123438
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.
亲爱的编辑,这封信讨论了多个机器人水面飞行器(RSV)在电池能量限制以及不确定性和干扰的情况下的长时间覆盖问题。本文提出了一种抗干扰能量感知控制方法,用于执行 RSV 的覆盖任务。首先,使用中心点 Voronoi 网格(CVT)来优化给定覆盖区域的划分。每辆车的最佳位置与 Voronoi 单元的中心点相对应。其次,通过两个电池能量控制障碍函数,设计出一种能量感知运动导向法,将每辆 RSV 驱动到最佳位置。最后,基于固定时间扩展状态观测器和非线性跟踪微分器,为每辆 RSV 设计了抗干扰固定时间动能控制法则,以跟踪所需的速度。通过这种控制方法,RSV 能够在给定的覆盖区域内实现长时间覆盖。仿真结果验证了所提出的多 RSV 抗干扰能量感知控制方法的有效性。
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引用次数: 0
A LiDAR Point Clouds Dataset of Ships in a Maritime Environment 海洋环境中船舶的激光雷达点云数据集
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2024.124275
Qiuyu Zhang;Lipeng Wang;Hao Meng;Wen Zhang;Genghua Huang
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.
本文首次介绍了由采集数据和模拟数据组成的船舶激光雷达点云数据集,以解决海洋应用中激光雷达数据稀缺的问题。收集到的数据是使用专门的海事激光雷达传感器在内河航道和广阔的海洋环境中获取的。模拟数据的生成方法是将一艘船置于激光雷达坐标系中,然后使用重新开发的 Blensor 对其进行扫描,该 Blensor 可模拟配备各种激光束的激光雷达传感器的运行。此外,我们还渲染了雾天和雨天的点云。为了描述真实的航运环境,我们通过迭代时间序列中每个点的波浪高程来模拟动态尾波。最后,通过输入我们的数据集,将服务于小型物体的网络迁移到船舶应用中。在物体检测实验中描述了模拟数据的积极作用,在单个物体跟踪实验中验证了作为噪声的尾波的负面影响。数据集可在 https://github.com/zqy411470859/ship_dataset 上获取。
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引用次数: 0
Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems 异步切换线性系统的插值无缓冲传输控制
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.124155
Shengao Lu;Tong Wu;Lixian Zhang;Jianan Yang;Ye Liang
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 $H_{infty}$ performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.
本文重新探讨了离散时间非确定性开关线性系统的无缓冲转移控制(BTC)问题。在开关系统无缓冲传输控制领域,首次考虑了异步开关的一般情况。提出了一种称为插值无缓冲转移控制(IBTC)的新方法,其中无缓冲转移控制器是由两个相邻的与模式相关的控制器增益组合而成的,一旦检测到切换,就对其进行有限步长的插值。与现有方法不同的是,IBTC 不一定要贯穿子系统的整个区间,而且还拥有时变控制器增益(灵活性更高,保守性更低),可通过控制综合实现整个切换系统的稳定性和其他性能。本文提出了通过 IBTC 确保底层系统稳定性和 $H_{infty}$ 性能的充分条件,并通过数值示例验证了理论结论。
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引用次数: 0
Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints 具有输出约束条件的时延非线性系统的规定性能跟踪控制
IF 11.8 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Pub Date : 2024-06-12 DOI: 10.1109/JAS.2023.123831
Jin-Xi Zhang;Kai-Di Xu;Qing-Guo Wang
The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper. Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy, without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.
本文探讨了在输出约束条件下对未知时延非线性系统进行规定性能跟踪控制的问题。与相关研究不同的是,本文仅对允许时间延迟假设了最基本的要求,即有界性和局部 Lipschitz 条件。此外,我们关注的是参考点事先未知的情况,这使得输出约束下的标准规定性能控制设计变得不可行。为了克服这些挑战,本文提出了一种新颖的鲁棒性规定性能控制方法。本文巧妙地构建了反向调节函数,并自动生成跟踪误差的性能包络线。此外,本文还建立了一个基于矛盾证明和障碍函数的统一性能分析框架,以揭示控制系统对时间延迟的内在鲁棒性。结果表明,系统输出以预设的平稳时间跟踪参考值,精度良好,没有违反约束。对一个两级化学反应器进行了对比模拟,以说明上述理论结论。
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引用次数: 0
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Ieee-Caa Journal of Automatica Sinica
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