系统与控制
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- [1] arXiv:2508.10318 [中文pdf, pdf, html, 其他]
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标题： 以恢复力增强为指标量化地震结构健康监测在电力系统震后恢复中的价值标题： Quantifying the Value of Seismic Structural Health Monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement评论： 21页。14图。主题： 系统与控制 (eess.SY)
地震后的电力网络(EPN)恢复对社区的恢复力至关重要。传统的恢复过程通常依赖于长时间且不精确的人工检查来进行损坏诊断，导致修复优先级不佳和持续的服务中断。地震结构健康监测(SSHM)通过实现更准确和及时的损坏评估，有望加快恢复进程。然而，SSHM的部署会带来成本，其系统级恢复力效益仍研究不足。本研究提出了一种概率模拟框架，以量化SSHM在提高EPN恢复力方面的价值。该框架包括基于网络配置、灾害强度、易损性函数和损坏-功能映射的地震损坏建模，并结合考虑资源限制、修复和转移时间的恢复模拟。系统功能通过基于图的岛屿检测和最优功率流分析进行评估。恢复力通过从功能恢复曲线中得出的恢复力缺乏(LoR)指标进行量化。SSHM通过改变修复调度中使用的损坏信息质量来纳入框架。不同的监测场景(例如，无SSHM基准、部分SSHM、具有不同准确性的全SSHM)使用混淆矩阵进行建模，以模拟损坏误分类。结果表明，通过SSHM提高的损坏意识显著加速了恢复，并将LoR降低了高达21%。这项工作为关键基础设施中SSHM的部署提供了基于证据的决策支持。
Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspections for damage diagnosis, leading to suboptimal repair prioritization and extended service disruptions. Seismic Structural Health Monitoring (SSHM) offers the potential to expedite recovery by enabling more accurate and timely damage assessment. However, SSHM deployment incurs costs, and its system-level resilience benefit remains underexplored. This study proposes a probabilistic simulation framework to quantify the value of SSHM for enhancing EPN resilience. The framework includes seismic damage modeling based on network configuration, hazard intensity, fragility functions, and damage-functionality mappings, combined with recovery simulations incorporating resource constraints, repair and transfer durations. System functionality is evaluated using graph-based island detection and optimal power flow analysis. Resilience is quantified via the Lack of Resilience (LoR) metric derived from the functionality restoration curve. SSHM is incorporated by altering the quality of damage information used in repair scheduling. Different monitoring scenarios (e.g., no-SSHM baseline, partial SSHM, full SSHM with various accuracies) are modeled using confusion matrices to simulate damage misclassification. Results show that improved damage awareness via SSHM significantly accelerates recovery and reduces LoR by up to 21%. This work supports evidence-based decisions for SSHM deployment in critical infrastructure.
- [2] arXiv:2508.10446 [中文pdf, pdf, html, 其他]
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标题： 一种用于在STPA中优先考虑不安全控制动作的结构化框架：eVTOL操作案例研究标题： A Structured Framework for Prioritizing Unsafe Control Actions in STPA: Case Study on eVTOL Operations主题： 系统与控制 (eess.SY)
系统理论过程分析（STPA）是一种广泛推荐的用于分析复杂系统安全的方法。 STPA可以根据分析的粒度和被分析系统的复杂性，识别出大量不安全的控制操作（UCAs）和需求。 管理大量的结果具有挑战性，尤其是在快速发展的开发生命周期中。 已经进行了大量研究以优化管理并优先处理STPA结果的效率。 然而，保持优先级的客观性以及传达优先的结果已成为常见的挑战。 在本文中，作者提出了一种补充方法，结合安全分析师和领域专家的输入，以更客观地优先处理UCAs。 这是通过评估每个UCA的严重性、发出UCA的每个控制器或决策者的影响力因素，以及根据不同因素评估UCA关键性的主题专家提供的排名来实现的。 此外，引入了蒙特卡洛模拟以减少主观性和相对性，从而实现对UCAs更客观的优先级排序。 作为更好地沟通优先级结果并规划系统开发下一步的手段，开发了一个动态缩放的优先级矩阵，以捕捉不同的一组优先UCAs。 该方法应用于一个实际项目，以提高电动垂直起降（eVTOL）的安全操作。 结果突出了需要优先考虑的关键UCAs，以实现更安全的eVTOL操作。 总共识别出318个UCAs。 根据优先级方法的应用，110个被认定为高优先级的UCAs，以加强系统设计。
Systems Theoretic Process Analysis (STPA) is a widely recommended method for analysing complex system safety. STPA can identify numerous Unsafe Control Actions (UCAs) and requirements depending on the level of granularity of the analysis and the complexity of the system being analysed. Managing numerous results is challenging, especially during a fast-paced development lifecycle. Extensive research has been done to optimize the efficiency of managing and prioritising the STPA results. However, maintaining the objectivity of prioritisation and communicating the prioritised results have become common challenges. In this paper, the authors present a complementary approach that incorporates inputs from both the safety analysts and domain experts to more objectively prioritise UCAs. This is done by evaluating the severity of each UCA, the impact factor of each controller or decision maker that issues the UCA, and the ranking provided by the subject matter experts who assess the UCA criticalities based on different factors. In addition, a Monte Carlo simulation is introduced to reduce subjectivity and relativity, thus enabling more objective prioritisation of the UCAs. As part of the approach to better communicate the prioritisation results and plan the next steps of system development, a dynamic-scaling prioritisation matrix was developed to capture different sets of prioritised UCAs. The approach was applied to a real project to improve the safe operations of Electric Vertical Take-off and Landing (eVTOL). The results highlighted critical UCAs that need to be prioritised for safer eVTOL operation. 318 UCAs were identified in total. Based on the application of the prioritisation methodology, 110 were recognized as high-priority UCAs to strengthen the system design.
- [3] arXiv:2508.10601 [中文pdf, pdf, 其他]
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标题： 光双势阱势强度最小处纳米粒子的反馈稳定化标题： Feedback stabilization of a nanoparticle at the intensity minimum of an optical double-well potentialVojtěch Mlynář (1), Salambô Dago (2), Jakob Rieser (2), Mario A. Ciampini (2), Markus Aspelmeyer (2 and 3), Nikolai Kiesel (2), Andreas Kugi (1 and 4), Andreas Deutschmann-Olek (1) ((1) Automation and Control Institute, TU Wien, Vienna, Austria, (2) University of Vienna, Faculty of Physics, Vienna Center for Quantum Science and Technology, Vienna, Austria, (3) Institute for Quantum Optics and Quantum Information Vienna, Austrian Academy of Sciences, Vienna, Austria, (4) AIT Austrian Institute of Technology, Vienna, Austria)主题： 系统与控制 (eess.SY) ; 量子物理 (quant-ph)
在本工作中，我们开发并分析自适应反馈控制策略，以稳定和限制纳米粒子在光学双势阱势能的不稳定强度最小值处。 对于在非线性光学势能中受噪声驱动的机械粒子，必须考虑不可避免的实验缺陷，例如测量非线性和光学装置的缓慢漂移。 为了解决这些问题，我们在不稳定平衡点附近简化模型，并采用间接自适应控制技术来动态跟踪势能景观的变化。 我们的方法导致了一个简单且高效的线性二次高斯（LQG）控制器，可以在快速且成本效益高的FPGA上实现，确保可访问性和可重复性。 我们证明了这种策略能够成功跟踪强度最小值，并显著降低纳米粒子的剩余状态方差，有效地降低了其质心温度。 虽然传统的光学陷阱依赖于在光强最大值处的约束光学力，但在光强最小值处的捕获可以减轻吸收加热，这对于先进的量子实验至关重要。 由于LQG控制自然扩展到量子领域，我们的结果为未来超越当前吸收加热限制的量子态制备实验提供了有希望的途径，如物质波干涉和量子引力界面的测试。
In this work, we develop and analyze adaptive feedback control strategies to stabilize and confine a nanoparticle at the unstable intensity minimum of an optical double-well potential. The resulting stochastic optimal control problem for a noise-driven mechanical particle in a nonlinear optical potential must account for unavoidable experimental imperfections such as measurement nonlinearities and slow drifts of the optical setup. To address these issues, we simplify the model in the vicinity of the unstable equilibrium and employ indirect adaptive control techniques to dynamically follow changes in the potential landscape. Our approach leads to a simple and efficient Linear Quadratic Gaussian (LQG) controller that can be implemented on fast and cost-effective FPGAs, ensuring accessibility and reproducibility. We demonstrate that this strategy successfully tracks the intensity minimum and significantly reduces the nanoparticle's residual state variance, effectively lowering its center-of-mass temperature. While conventional optical traps rely on confining optical forces in the light field at the intensity maxima, trapping at intensity minima mitigates absorption heating, which is crucial for advanced quantum experiments. Since LQG control naturally extends into the quantum regime, our results provide a promising pathway for future experiments on quantum state preparation beyond the current absorption heating limitation, like matter-wave interference and tests of the quantum-gravity interface.
- [4] arXiv:2508.10679 [中文pdf, pdf, 其他]
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标题： 一种针对固定频率空调需求响应参与的鲁棒优化方法标题： A Robust Optimization Approach for Demand Response Participation of Fixed-Frequency Air Conditioners主题： 系统与控制 (eess.SY)
随着可再生能源在新兴电力系统中渗透率的持续增加，系统调峰压力显著加剧。 在这种背景下，需求侧资源，特别是空调负荷，因其巨大的调节潜力和快速的响应能力而受到广泛关注，使其成为提供辅助调峰服务的有前景的候选者。 本研究聚焦于固定频率空调（FFACs），并提出了它们参与需求响应（DR）项目的优化模型和求解方法。 首先，基于马尔可夫假设，开发了一个FFACs的概率响应模型。 其次，通过对此概率模型进行采样，得到了在分散控制下FFAC集群的总功率消耗。 随后，构建了一个鲁棒优化模型，以在DR事件中最大化管理FFAC集群的聚合商的利润，同时考虑聚合响应功率。 该模型明确考虑了温度不确定性，以确保在鲁棒意义上的用户舒适度。 最后，利用所提出模型的结构，将其重新表述为混合整数线性规划（MILP）问题，并使用商业优化求解器进行求解。 仿真结果验证了所提出模型和求解方法的有效性。
With the continuous increase in the penetration of renewable energy in the emerging power systems, the pressure on system peak regulation has been significantly intensified. Against this backdrop, demand side resources particularly air conditioning loads have garnered considerable attention for their substantial regulation potential and fast response capabilities, making them promising candidates for providing auxiliary peak shaving services. This study focuses on fixed frequency air conditioners (FFACs) and proposes an optimization model and solution method for their participation in demand response (DR) programs. First, a probabilistic response model for FFACs is developed based on the Markov assumption. Second, by sampling this probabilistic model, the aggregate power consumption of an FFAC cluster under decentralized control is obtained. Subsequently, a robust optimization model is formulated to maximize the profit of an aggregator managing the FFAC cluster during DR events, taking into account the aggregated response power. The model explicitly considers temperature uncertainty to ensure user comfort in a robust sense. Finally, leveraging the structure of the proposed model, it is reformulated as a mixed-integer linear programming (MILP) problem and solved using a commercial optimization solver. Simulation results validate the effectiveness of the proposed model and solution approach.
- [5] arXiv:2508.10705 [中文pdf, pdf, html, 其他]
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标题： 基于评分的条件扩散模型的海上风电场集群台风条件概率预测方法标题： Probabilistic Forecasting Method for Offshore Wind Farm Cluster under Typhoon Conditions: a Score-Based Conditional Diffusion Model主题： 系统与控制 (eess.SY)
在台风条件下，海上风电（OWP）表现出显著的波动，这对电力系统的安全运行构成了重大挑战。因此，准确预测OWP至关重要。然而，历史台风数据的固有稀缺性和OWP的随机性使得传统点预测方法尤其困难且不足。为解决这一挑战，并为电网运营商提供决策所需的全面信息，本研究提出了一种基于评分的条件扩散模型（SCDM），用于台风期间OWP的概率预测。首先，采用知识图算法将历史台风路径嵌入为向量。然后，构建一个确定性网络，基于这些向量嵌入预测台风条件下的风力发电量。最后，为更好地表征预测误差，开发了一个去噪网络。该方法的核心是一种均值回归随机微分方程（SDE），它将复杂的误差分布转换为标准高斯分布，从而能够使用反向时间SDE对预测误差进行采样。通过将确定性预测与采样的误差相结合，重建概率预测结果。所提出的方法使用一组9个海上风电场的真实数据进行评估。结果表明，在台风条件下，我们的方法在确定性和概率指标上均优于基线模型，验证了该方法的有效性。
Offshore wind power (OWP) exhibits significant fluctuations under typhoon conditions, posing substantial challenges to the secure operation of power systems. Accurate forecasting of OWP is therefore essential. However, the inherent scarcity of historical typhoon data and stochasticity of OWP render traditional point forecasting methods particularly difficult and inadequate. To address this challenge and provide grid operators with the comprehensive information necessary for decision-making, this study proposes a score-based conditional diffusion model (SCDM) for probabilistic forecasting of OWP during typhoon events. First, a knowledge graph algorithm is employed to embed historical typhoon paths as vectors. Then, a deterministic network is constructed to predict the wind power under typhoon conditions based on these vector embeddings. Finally, to better characterize prediction errors, a denoising network is developed. At the core of this approach is a mean-reverting stochastic differential equation (SDE), which transforms complex error distributions into a standard Gaussian, enabling the sampling of forecasting errors using a reverse-time SDE. The probabilistic forecasting results are reconstructed by combining deterministic forecasts with sampled errors. The proposed method is evaluated using real-world data from a cluster of 9 offshore wind farms. Results demonstrate that under typhoon conditions, our approach outperforms baseline models for both deterministic and probabilistic metrics, verifying the effectiveness of the approach.
- [6] arXiv:2508.10730 [中文pdf, pdf, html, 其他]
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标题： 基于静态被动EMS的多功能极化覆盖控制标题： Multi-Functional Polarization-Based Coverage Control through Static Passive EMSs主题： 系统与控制 (eess.SY)
一种创新的多功能静态被动电磁皮肤（SP-EMS）解决方案被提出，以在反射状态下同时支持两个独立的波操纵功能，当由两个在同一频率下工作但处于不同极化状态的电磁源照射时，仅使用EMS孔径上的单一元原子排列。 为此，首先设计了一个简单的参考元原子，以实现对局部反射张量每个极化分量的精确和独立控制。 随后，通过解决一个全局优化问题，进行了多极化（MP）SP-EMSs（MP-SP-EMSs）的宏观尺度合成，其中使用了一种定制版本的系统设计（SbD）技术来最小化一个成本函数，该成本函数数学编码了每个极化分别的要求。 报告了一组数值和实验测试的代表性结果，以评估基于极化多样性的多功能EMS的可行性，以及所提出的MP-SP-EMSs合成方法的有效性和鲁棒性。
An innovative multi-functional static-passive electromagnetic skin (SP-EMS) solution is proposed to simultaneously support, in reflection, two independent wave-manipulation functionalities with a single meta-atoms arrangement on the EMS aperture when illuminated by two EM sources operating at the same frequency, but working in different polarization states. Towards this end, a simple reference meta-atom is designed first to enable an accurate and independent control of each polarization component of the local reflection tensor. Successively, the macro-scale synthesis of multi-polarization (MP) SP-EMSs (MP-SP-EMSs) is carried out by solving a global optimization problem where a cost function, which mathematically codes separate requirements for each polarization, is minimized with a customized version of the system-by-design (SbD) technique. Representative results from a set of numerical and experimental tests are reported to assess the feasibility of a multi-function EMS based on polarization diversity as well as the effectiveness and the robustness of the proposed method for the synthesis of MP-SP-EMSs.
- [7] arXiv:2508.10849 [中文pdf, pdf, html, 其他]
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标题： 融合陆地与非陆地网络的可持续6G运营：一种时延感知的多层小区切换方法标题： Integrating Terrestrial and Non-Terrestrial Networks for Sustainable 6G Operations: A Latency-Aware Multi-Tier Cell-Switching Approach评论： 9页，6图主题： 系统与控制 (eess.SY)
可持续性在现代蜂窝网络中至关重要，这些网络面临着移动流量增加和无线技术进步带来的显著能耗挑战。 单元切换在文献中已被确立为一种有效解决方案，但在通过陆地网络（TN）实施时，会遇到容量不足和覆盖范围有限等限制。 本研究通过将非陆地网络（NTN）集成到TN中，从而增强了单元切换，这些非陆地网络包括卫星（首次用于单元切换）、高空平台站（HAPS）和无人飞行器（UAV）。 这种集成通过扩展容量、增强覆盖范围和提高操作灵活性，显著提高了节能效果。 我们引入了一种多层单元切换方法，通过在网络层之间动态卸载用户来有效管理能源并最小化延迟，采用上下文感知策略满足多样化的用户需求。 此外，我们探讨了人工智能（AI），特别是生成式AI，在通过数据压缩、不同网络层之间的切换优化以及提高设备兼容性来优化网络效率中的作用，进一步提高了单元切换操作的适应性和能效。 案例研究证实了网络功耗和用户满意度的显著提升，展示了我们方法在未来网络中的潜力。
Sustainability is paramount in modern cellular networks, which face significant energy consumption challenges from rising mobile traffic and advancements in wireless technology. Cell-switching, well-established in literature as an effective solution, encounters limitations such as inadequate capacity and limited coverage when implemented through terrestrial networks (TN). This study enhances cell-switching by integrating non-terrestrial networks (NTN), including satellites (used for cell-switching for the first time), high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAVs) into TN. This integration significantly boosts energy savings by expanding capacity, enhancing coverage, and increasing operational flexibility. We introduce a multi-tier cell-switching approach that dynamically offloads users across network layers to manage energy effectively and minimize delays, accommodating diverse user demands with a context aware strategy. Additionally, we explore the role of artificial intelligence (AI), particularly generative AI, in optimizing network efficiency through data compression, handover optimization between different network layers, and enhancing device compatibility, further improving the adaptability and energy efficiency of cell-switching operations. A case study confirms substantial improvements in network power consumption and user satisfaction, demonstrating the potential of our approach for future networks.
- [8] arXiv:2508.10891 [中文pdf, pdf, html, 其他]
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标题： 车队燃油消耗：文献综述标题： Fuel Consumption in Platoons: A Literature Review主题： 系统与控制 (eess.SY)
编队已成为一种有前景的策略，用于提高自动化车辆系统的燃油效率，对减少排放和运营成本具有重要意义。 尽管现有的车辆编队文献主要关注个体方面，如空气动力学阻力减少或特定控制策略，本文采用更全面的方法，将影响编队中燃油节省的广泛因素和组件结合起来。 在本文献综述中，我们考察了编队对燃油消耗的影响，重点介绍了编队系统的关键组成部分、影响燃油节省的因素和相关方、估算燃油使用的方法，以及编队不稳定对效率的影响。 此外，我们研究了减少空气动力学阻力、车辆协调以及在现实条件下不稳定性带来的挑战。 通过综合近期研究的见解，本文提供了编队技术最新进展的全面概述，并突出了未来研究在现实场景中最大化燃油节省的挑战和机遇。
Platooning has emerged as a promising strategy for improving fuel efficiency in automated vehicle systems, with significant implications for reducing emissions and operational costs. While existing literature on vehicle platooning primarily focuses on individual aspects such as aerodynamic drag reduction or specific control strategies, this work takes a more comprehensive approach by bringing together a wide range of factors and components that contribute to fuel savings in platoons. In this literature review, we examine the impact of platooning on fuel consumption, highlighting the key components of platoon systems, the factors and actors influencing fuel savings, methods for estimating fuel use, and the effect of platoon instability on efficiency. Furthermore, we study the role of reduced aerodynamic drag, vehicle coordination, and the challenges posed by instability in real-world conditions. By compiling insights from recent studies, this work provides a comprehensive overview of the latest advancements in platooning technologies and highlights both the challenges and opportunities for future research to maximize fuel savings in real-world scenarios.
新提交 (展示 8 之 8 条目 )
- [9] arXiv:2508.10035 (交叉列表自 cs.CR) [中文pdf, pdf, 其他]
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标题： 基于神经网络的智能电网家庭能源系统中FDI攻击的检测与多类分类标题： Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems评论： 17页，7图主题： 密码学与安全 (cs.CR) ; 机器学习 (cs.LG) ; 系统与控制 (eess.SY)
虚假数据注入攻击（FDIAs）对智能电网基础设施，特别是家庭局域网络（HANs）构成重大威胁，因为在HANs中实时监控和控制得到了广泛应用。由于HANs的安全控制相对宽松且广泛可用，攻击者将其视为一个有吸引力的切入点，以操纵聚合需求模式，这最终会传播并破坏更广泛的电网操作。这些攻击破坏了智能电表数据的完整性，使恶意行为者能够在不触发传统警报的情况下操纵消耗值，从而在住宅和公用事业规模的基础设施中造成严重的漏洞。本文提出了一种基于机器学习的框架，使用住宅能源数据来检测和分类FDIAs。轻量级人工神经网络（ANN）提供实时检测，通过使用能源消耗、成本和时间上下文的关键特征进行工作。为了对不同类型的攻击进行分类，训练了一个双向LSTM来通过学习数据中的序列依赖性来识别正常、梯形和S型攻击形状。生成了一个合成的时间序列数据集来模拟现实的家庭行为。实验结果表明，所提出的模型在识别和分类FDIAs方面是有效的，为在边缘增强电网弹性提供了可扩展的解决方案。这项工作有助于构建智能、数据驱动的防御机制，从住宅端点加强智能电网网络安全。
False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures, particularly Home Area Networks (HANs), where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent security controls and widespread availability of HANs, attackers view them as an attractive entry point to manipulate aggregated demand patterns, which can ultimately propagate and disrupt broader grid operations. These attacks undermine the integrity of smart meter data, enabling malicious actors to manipulate consumption values without activating conventional alarms, thereby creating serious vulnerabilities across both residential and utility-scale infrastructures. This paper presents a machine learning-based framework for both the detection and classification of FDIAs using residential energy data. A real-time detection is provided by the lightweight Artificial Neural Network (ANN), which works by using the most vital features of energy consumption, cost, and time context. For the classification of different attack types, a Bidirectional LSTM is trained to recognize normal, trapezoidal, and sigmoid attack shapes through learning sequential dependencies in the data. A synthetic time-series dataset was generated to emulate realistic household behaviour. Experimental results demonstrate that the proposed models are effective in identifying and classifying FDIAs, offering a scalable solution for enhancing grid resilience at the edge. This work contributes toward building intelligent, data-driven defence mechanisms that strengthen smart grid cybersecurity from residential endpoints.
- [10] arXiv:2508.10150 (交叉列表自 math.OC) [中文pdf, pdf, html, 其他]
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标题： 输出反馈遗憾最优控制中的分布鲁棒性标题： Distributional Robustness in Output Feedback Regret-Optimal Control评论： 在第11届IFAC鲁棒控制设计研讨会上发表主题： 优化与控制 (math.OC) ; 系统与控制 (eess.SY)
本文研究了在存在加性扰动和测量噪声的线性系统中，采用净化输出反馈的分布鲁棒后悔最优（DRRO）控制。这些不确定性（包括初始系统状态）被假设为随机的，并且根据一个未知的联合概率分布，在Wasserstein不确定集内分布。我们设计了仿射控制器，以最小化该集合中所有分布的最坏情况期望后悔值。期望后悔值定义为由一个仿射因果控制器产生的期望成本与在初始时刻具有扰动轨迹完美知识的最优非因果控制器产生的期望成本之间的差值。利用分布鲁棒优化中的对偶理论，我们得出了涉及一般二次目标函数的最坏情况期望问题的强对偶结果，从而将DRRO控制问题精确地重新表述为半定规划（SDP）。专注于一种这样的重新表述，我们消除了某些决策变量。这种技术还允许将SDP进一步等价地重新表述为分布式优化问题，具有提高可扩展性的潜力。
This paper studies distributionally robust regret-optimal (DRRO) control with purified output feedback for linear systems subject to additive disturbances and measurement noise. These uncertainties (including the initial system state) are assumed to be stochastic and distributed according to an unknown joint probability distribution within a Wasserstein ambiguity set. We design affine controllers to minimise the worst-case expected regret over all distributions in this set. The expected regret is defined as the difference between an expected cost incurred by an affine causal controller and the expected cost incurred by the optimal noncausal controller with perfect knowledge of the disturbance trajectory at the outset. Leveraging the duality theory in distributionally robust optimisation, we derive strong duality results for worst-case expectation problems involving general quadratic objective functions, enabling exact reformulations of the DRRO control problem as semidefinite programs (SDPs). Focusing on one such reformulation, we eliminate certain decision variables. This technique also permits a further equivalent reformulation of the SDP as a distributed optimisation problem, with potential to enhance scalability.
- [11] arXiv:2508.10203 (交叉列表自 cs.RO) [中文pdf, pdf, html, 其他]
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标题： 基于凸集时空图的系统约束表述与无碰撞轨迹规划标题： Systematic Constraint Formulation and Collision-Free Trajectory Planning Using Space-Time Graphs of Convex Sets评论： 21页，参考文献，20幅图主题： 机器人技术 (cs.RO) ; 系统与控制 (eess.SY) ; 优化与控制 (math.OC)
在本文中，我们在杂乱的动态环境中创建最优、无碰撞、随时间变化的轨迹。 许多空间和时间约束使得为数值求解器找到一个初始猜测变得困难。 凸集图（GCS）以及最近开发的空间-时间凸集图公式（ST-GCS）使我们能够在不向求解器提供初始猜测的情况下生成最优最小距离无碰撞轨迹。 我们还探讨了通用GCS兼容约束的推导，并记录了一种将通用约束适应到框架中的直观策略。 我们证明当环境静态时，ST-GCS产生的轨迹与标准GCS公式产生相同的结果。 然后我们展示ST-GCS在动态环境中运行以找到最小距离无碰撞轨迹。
In this paper, we create optimal, collision-free, time-dependent trajectories through cluttered dynamic environments. The many spatial and temporal constraints make finding an initial guess for a numerical solver difficult. Graphs of Convex Sets (GCS) and the recently developed Space-Time Graphs of Convex Sets formulation (ST-GCS) enable us to generate optimal minimum distance collision-free trajectories without providing an initial guess to the solver. We also explore the derivation of general GCS-compatible constraints and document an intuitive strategy for adapting general constraints to the framework. We show that ST-GCS produces equivalent trajectories to the standard GCS formulation when the environment is static. We then show ST-GCS operating in dynamic environments to find minimum distance collision-free trajectories.
- [12] arXiv:2508.10423 (交叉列表自 cs.RO) [中文pdf, pdf, html, 其他]
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标题： MASH：用于单个类人机器人移动的协作异构多智能体强化学习标题： MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion主题： 机器人技术 (cs.RO) ; 人工智能 (cs.AI) ; 系统与控制 (eess.SY)
本文提出了一种新方法，通过合作异构多智能体深度强化学习（MARL）来增强单个人形机器人的运动能力。 尽管大多数现有方法通常为单个人形机器人使用单智能体强化学习算法或为多机器人系统任务使用MARL算法，我们提出了一种不同的范式：将合作异构MARL应用于优化单个人形机器人的运动能力。 所提出的方法，单人形机器人运动的多智能体强化学习（MASH），将每个肢体（腿和臂）视为一个独立的智能体，在探索机器人动作空间的同时共享一个全局评论家以进行合作学习。 实验表明，MASH加速了训练收敛并提高了全身协作能力，优于传统的单智能体强化学习方法。 这项工作推进了MARL在单人形机器人控制中的集成，为高效的运动策略提供了新的见解。
This paper proposes a novel method to enhance locomotion for a single humanoid robot through cooperative-heterogeneous multi-agent deep reinforcement learning (MARL). While most existing methods typically employ single-agent reinforcement learning algorithms for a single humanoid robot or MARL algorithms for multi-robot system tasks, we propose a distinct paradigm: applying cooperative-heterogeneous MARL to optimize locomotion for a single humanoid robot. The proposed method, multi-agent reinforcement learning for single humanoid locomotion (MASH), treats each limb (legs and arms) as an independent agent that explores the robot's action space while sharing a global critic for cooperative learning. Experiments demonstrate that MASH accelerates training convergence and improves whole-body cooperation ability, outperforming conventional single-agent reinforcement learning methods. This work advances the integration of MARL into single-humanoid-robot control, offering new insights into efficient locomotion strategies.
- [13] arXiv:2508.10515 (交叉列表自 physics.comp-ph) [中文pdf, pdf, html, 其他]
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标题： IGBT模块中焊层退化和温度监测的虚拟传感标题： Virtual Sensing for Solder Layer Degradation and Temperature Monitoring in IGBT Modules评论： 安德里亚·乌尔戈洛和莫妮卡·斯皮茨共同为本研究做出了贡献主题： 计算物理 (physics.comp-ph) ; 计算工程、金融与科学 (cs.CE) ; 机器学习 (cs.LG) ; 系统与控制 (eess.SY)
监测绝缘栅双极晶体管（IGBT）模块的退化状态对于确保电力电子系统的可靠性和使用寿命至关重要，尤其是在安全关键和高性能应用中。 然而，由于内部组件的物理不可达性和恶劣环境，直接测量关键退化指标——如结温、焊料疲劳或分层——仍然具有挑战性。 在此背景下，基于机器学习的虚拟传感提供了一个有前景的替代方案，通过从可行的传感器布置到相关但不可达的位置之间建立桥梁。 本文探讨了基于有限数量的物理传感器估计焊料层退化状态以及相应完整温度图的可行性。 基于特定退化模式的合成数据，我们获得了退化焊料区域估计的高精度（1.17%的平均绝对误差），并且能够以最大相对误差4.56%（对应平均相对误差0.37%）再现IGBT的表面温度。
Monitoring the degradation state of Insulated Gate Bipolar Transistor (IGBT) modules is essential for ensuring the reliability and longevity of power electronic systems, especially in safety-critical and high-performance applications. However, direct measurement of key degradation indicators - such as junction temperature, solder fatigue or delamination - remains challenging due to the physical inaccessibility of internal components and the harsh environment. In this context, machine learning-based virtual sensing offers a promising alternative by bridging the gap from feasible sensor placement to the relevant but inaccessible locations. This paper explores the feasibility of estimating the degradation state of solder layers, and the corresponding full temperature maps based on a limited number of physical sensors. Based on synthetic data of a specific degradation mode, we obtain a high accuracy in the estimation of the degraded solder area (1.17% mean absolute error), and are able to reproduce the surface temperature of the IGBT with a maximum relative error of 4.56% (corresponding to an average relative error of 0.37%).
- [14] arXiv:2508.10608 (交叉列表自 cs.LG) [中文pdf, pdf, 其他]
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标题： 方差减少的多目标强化学习策略梯度方法标题： Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning评论： 7页，4图主题： 机器学习 (cs.LG) ; 系统与控制 (eess.SY) ; 优化与控制 (math.OC) ; 统计理论 (math.ST)
多目标强化学习（MORL）是传统强化学习（RL）的一种推广，旨在同时优化多个有时相互冲突的目标，而不是专注于单一奖励。 这种方法在复杂的决策场景中至关重要，其中代理必须在各种目标之间权衡，例如在最大化性能的同时最小化成本。 我们考虑使用非线性标量化函数组合目标的MORL问题。 就像在标准RL中一样，策略梯度方法（PGMs）是处理MORL中大型和连续状态-动作空间最有效的方法之一。 然而，现有的MORL策略梯度方法存在样本效率低的问题，需要大量数据才能有效。 以前解决这个问题的尝试依赖于过于严格的假设，在扩展到大型状态-动作空间时失去了PGMs的优势。 在本工作中，我们通过实施方差减少技术来提高样本效率，在保持一般假设的同时降低策略梯度的样本复杂度。
Multi-Objective Reinforcement Learning (MORL) is a generalization of traditional Reinforcement Learning (RL) that aims to optimize multiple, often conflicting objectives simultaneously rather than focusing on a single reward. This approach is crucial in complex decision-making scenarios where agents must balance trade-offs between various goals, such as maximizing performance while minimizing costs. We consider the problem of MORL where the objectives are combined using a non-linear scalarization function. Just like in standard RL, policy gradient methods (PGMs) are amongst the most effective for handling large and continuous state-action spaces in MORL. However, existing PGMs for MORL suffer from high sample inefficiency, requiring large amounts of data to be effective. Previous attempts to solve this problem rely on overly strict assumptions, losing PGMs' benefits in scalability to large state-action spaces. In this work, we address the issue of sample efficiency by implementing variance-reduction techniques to reduce the sample complexity of policy gradients while maintaining general assumptions.
- [15] arXiv:2508.10634 (交叉列表自 cs.RO) [中文pdf, pdf, html, 其他]
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标题： 基于安全鲁棒自适应控制的深度神经网络合成，用于轮式移动机器人的可靠运行标题： Synthesis of Deep Neural Networks with Safe Robust Adaptive Control for Reliable Operation of Wheeled Mobile Robots主题： 机器人技术 (cs.RO) ; 系统与控制 (eess.SY)
深度神经网络（DNNs）可以通过避免动态建模的需要，实现精确控制的同时保持较低的计算成本。 然而，对于受严格国际标准约束且容易发生故障和干扰的重型轮式移动机器人（WMRs），此类黑盒方法的部署仍然具有挑战性。 我们为重型WMRs设计了一种分层控制策略，由两个具有不同权限级别的安全层进行监控。 为此，训练并部署了一个DNN策略作为主要控制方案，在正常运行条件下提供高精度性能。 当外部干扰出现并达到一定强度，导致系统性能低于预定义阈值时，低级安全层会通过停用主要控制策略并激活无模型鲁棒自适应控制（RAC）策略来介入。 这种转换使系统能够在有效管理系统鲁棒性与响应性之间固有权衡的同时继续运行。 无论使用哪种控制策略，高级安全层都会在运行过程中持续监控系统性能。 只有当干扰变得足够严重，使得补偿不再可行，并且继续运行会危及系统或其环境时，才会启动关机。 所提出的DNN和RAC策略的综合方案在一定程度上保证了整个WMR系统的统一指数稳定性。 通过使用6,000公斤的WMR进行实时实验，进一步验证了所提出方法的有效性。
Deep neural networks (DNNs) can enable precise control while maintaining low computational costs by circumventing the need for dynamic modeling. However, the deployment of such black-box approaches remains challenging for heavy-duty wheeled mobile robots (WMRs), which are subject to strict international standards and prone to faults and disturbances. We designed a hierarchical control policy for heavy-duty WMRs, monitored by two safety layers with differing levels of authority. To this end, a DNN policy was trained and deployed as the primary control strategy, providing high-precision performance under nominal operating conditions. When external disturbances arise and reach a level of intensity such that the system performance falls below a predefined threshold, a low-level safety layer intervenes by deactivating the primary control policy and activating a model-free robust adaptive control (RAC) policy. This transition enables the system to continue operating while ensuring stability by effectively managing the inherent trade-off between system robustness and responsiveness. Regardless of the control policy in use, a high-level safety layer continuously monitors system performance during operation. It initiates a shutdown only when disturbances become sufficiently severe such that compensation is no longer viable and continued operation would jeopardize the system or its environment. The proposed synthesis of DNN and RAC policy guarantees uniform exponential stability of the entire WMR system while adhering to safety standards to some extent. The effectiveness of the proposed approach was further validated through real-time experiments using a 6,000 kg WMR.
- [16] arXiv:2508.10724 (交叉列表自 econ.TH) [中文pdf, pdf, html, 其他]
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标题： 软预算约束下的双重工具筛选标题： Two-Instrument Screening under Soft Budget Constraints评论： arXiv管理员备注：与arXiv:2508.02171文本重叠主题： 理论经济学 (econ.TH) ; 系统与控制 (eess.SY) ; 优化与控制 (math.OC)
我们研究多层级公共财政中的软预算约束，当上级政府使用两种工具：事前拨款计划和事后救助。 在凸救助成本和标准基本假设下，三阶段领导-跟随问题简化为一维筛选，仅有一个分配指标：实际救助的上限。 基于危险率的描述提供了一个统一规则，涵盖了（i）无救助，（ii）有承诺的阈值上限，以及（iii）无承诺的阈值-线性-上限。 消除救助的临界条件比较原点处的边际成本与虚拟权重的上确界，而比较静态分析显示，更大的曲率会收紧上限，而自由裁量会通过降低有效拨款权重将转移支付向前推动。 该框架为机制设计提供了一个可移植的基准，并为政府间财政政策和实证研究提供了可检验的含义。
We study soft budget constraints in multi-tier public finance when an upper-tier government uses two instruments: an ex-ante grant schedule and an ex-post rescue. Under convex rescue costs and standard primitives, the three-stage leader-follower problem collapses to one dimensional screening with a single allocation index: the cap on realized rescue. A hazard-based characterization delivers a unified rule that nests (i) no rescue, (ii) a threshold-cap with commitment, and (iii) a threshold--linear--cap without commitment. The knife-edge for eliminating bailouts compares the marginal cost at the origin to the supremum of a virtual weight, and the comparative statics show how greater curvature tightens caps while discretion shifts transfers toward front loading by lowering the effective grant weight. The framework provides a portable benchmark for mechanism design and yields testable implications for policy and empirical work on intergovernmental finance.
- [17] arXiv:2508.10733 (交叉列表自 cs.CY) [中文pdf, pdf, html, 其他]
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标题： 使用SUMO中的转弯流量数据进行交通交叉口仿真：多伦多交叉口案例研究标题： Traffic Intersection Simulation Using Turning Movement Count Data in SUMO: A Case Study of Toronto Intersections评论： 发表于2025年第21届DCOSS-IoT，代码可在GitHub上获取：https://github.com/ANTS-OntarioTechU/CrossFlow主题： 计算机与社会 (cs.CY) ; 系统与控制 (eess.SY)
城市交通仿真在规划、建模和分析道路网络中至关重要。 然而，仿真的真实程度在很大程度上取决于输入数据的质量。 本文介绍了一种交叉口交通仿真工具，该工具利用多伦多市的真实车辆转弯移动次数（TMC）数据，通过城市移动仿真（SUMO）在个体或多个交叉口处对城市环境中的交通进行建模。 本研究中进行的仿真特别关注于交叉口级别的交通生成，而无需通过网络创建完整的车辆路线。 这也有助于将网络的复杂性降到最低。 模拟的交通被与实际数据进行对比，以显示仿真能够准确再现真实的交叉口流量。 这验证了真实数据可以驱动实际的仿真，这些场景可以替代在开发新的交通相关方法时广泛使用的合成或随机生成的数据。 这是第一个通过易于使用的图形用户界面将多伦多的TMC数据集成到SUMO中的工具。 这项工作为数据驱动的交通仿真研究和交通规划社区做出了贡献。 它为交通工程师提供了一个框架，利用现成的汇总交通数据来评估交叉口设计和交通信号优化策略。
Urban traffic simulation is vital in planning, modeling, and analyzing road networks. However, the realism of a simulation depends extensively on the quality of input data. This paper presents an intersection traffic simulation tool that leverages real-world vehicle turning movement count (TMC) data from the City of Toronto to model traffic in an urban environment at an individual or multiple intersections using Simulation of Urban MObility (SUMO). The simulation performed in this research focuses specifically on intersection-level traffic generation without creating full vehicle routes through the network. This also helps keep the network's complexity to a minimum. The simulated traffic is evaluated against actual data to show that the simulation closely reproduces real intersection flows. This validates that the real data can drive practical simulations, and these scenarios can replace synthetic or random generated data, which is prominently used in developing new traffic-related methodologies. This is the first tool to integrate TMC data from Toronto into SUMO via an easy-to-use Graphical User Interface. This work contributes to the research and traffic planning community on data-driven traffic simulation. It provides transportation engineers with a framework to evaluate intersection design and traffic signal optimization strategies using readily available aggregate traffic data.
- [18] arXiv:2508.10780 (交叉列表自 cs.RO) [中文pdf, pdf, html, 其他]
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标题： 冗余机器人学习任务执行层次结构标题： Learning Task Execution Hierarchies for Redundant Robots主题： 机器人技术 (cs.RO) ; 系统与控制 (eess.SY)
现代机器人系统，如移动机械臂、人形机器人和带手臂的空中机器人，通常具有高冗余度，使其能够同时执行多项任务。 管理这种冗余是实现可靠和灵活行为的关键。 一种广泛使用的方法是任务堆叠（SoT），它在统一框架内按优先级组织控制目标。 然而，传统的SoT是由专家手动设计的，限制了它们的适应性和可访问性。 本文介绍了一种新框架，可以从用户定义的目标中自动学习SoT的层次结构和参数。 通过结合强化学习和遗传编程，系统在无需人工干预的情况下发现任务优先级和控制策略。 基于直观指标的成本函数，如精度、安全性和执行时间，指导学习过程。 我们通过在移动-YuMi平台上的仿真和实验验证了我们的方法，该平台是一个高冗余的双臂移动机械臂。 结果表明，学习到的SoT使机器人能够动态适应变化的环境和输入，在保持强大任务执行的同时平衡竞争目标。 这种方法为复杂机器人的冗余管理提供了一个通用且用户友好的解决方案，推动了以人为中心的机器人编程，并减少了对专家设计的需求。
Modern robotic systems, such as mobile manipulators, humanoids, and aerial robots with arms, often possess high redundancy, enabling them to perform multiple tasks simultaneously. Managing this redundancy is key to achieving reliable and flexible behavior. A widely used approach is the Stack of Tasks (SoT), which organizes control objectives by priority within a unified framework. However, traditional SoTs are manually designed by experts, limiting their adaptability and accessibility. This paper introduces a novel framework that automatically learns both the hierarchy and parameters of a SoT from user-defined objectives. By combining Reinforcement Learning and Genetic Programming, the system discovers task priorities and control strategies without manual intervention. A cost function based on intuitive metrics such as precision, safety, and execution time guides the learning process. We validate our method through simulations and experiments on the mobile-YuMi platform, a dual-arm mobile manipulator with high redundancy. Results show that the learned SoTs enable the robot to dynamically adapt to changing environments and inputs, balancing competing objectives while maintaining robust task execution. This approach provides a general and user-friendly solution for redundancy management in complex robots, advancing human-centered robot programming and reducing the need for expert design.
- [19] arXiv:2508.10867 (交叉列表自 cs.RO) [中文pdf, pdf, html, 其他]
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标题： CVIRO：一种一致且紧密耦合的李群视觉-惯性-测距里程计标题： CVIRO: A Consistent and Tightly-Coupled Visual-Inertial-Ranging Odometry on Lie Groups主题： 机器人技术 (cs.RO) ; 系统与控制 (eess.SY)
超宽带（UWB）被广泛用于减轻视觉惯性里程计（VIO）系统中的漂移。 一致性对于确保UWB辅助的VIO系统的估计准确性至关重要。 不一致的估计器会降低定位性能，其中不一致主要源于两个主要原因：（1）估计器无法保持正确的系统可观测性，以及（2）假设UWB锚点位置已知，导致对校准不确定性处理不当。 在本文中，我们提出了一种基于李群的一致且紧密耦合的视觉惯性测距里程计（CVIRO）系统。 我们的方法将UWB锚点状态纳入系统状态，显式考虑UWB校准不确定性，并实现机器人和锚点状态的联合和一致估计。 此外，通过利用李群的不变误差特性来确保可观测性一致性。 我们分析证明，CVIRO算法自然保持系统的正确不可观测子空间，从而保持估计一致性。 大量的仿真和实验表明，与现有方法相比，CVIRO实现了更优的定位精度和一致性。
Ultra Wideband (UWB) is widely used to mitigate drift in visual-inertial odometry (VIO) systems. Consistency is crucial for ensuring the estimation accuracy of a UWBaided VIO system. An inconsistent estimator can degrade localization performance, where the inconsistency primarily arises from two main factors: (1) the estimator fails to preserve the correct system observability, and (2) UWB anchor positions are assumed to be known, leading to improper neglect of calibration uncertainty. In this paper, we propose a consistent and tightly-coupled visual-inertial-ranging odometry (CVIRO) system based on the Lie group. Our method incorporates the UWB anchor state into the system state, explicitly accounting for UWB calibration uncertainty and enabling the joint and consistent estimation of both robot and anchor states. Furthermore, observability consistency is ensured by leveraging the invariant error properties of the Lie group. We analytically prove that the CVIRO algorithm naturally maintains the system's correct unobservable subspace, thereby preserving estimation consistency. Extensive simulations and experiments demonstrate that CVIRO achieves superior localization accuracy and consistency compared to existing methods.
交叉提交 (展示 11 之 11 条目 )
- [20] arXiv:2310.11790 (替换) [中文pdf, pdf, html, 其他]
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标题： MIMO系统辨识的有限样本性能分析标题： Finite Sample Performance Analysis of MIMO Systems Identification评论： 14页，6图主题： 系统与控制 (eess.SY)
本文关注的是具有p个输入和m个输出的n维离散时间多输入多输出（MIMO）线性时不变系统的有限样本识别性能。我们证明，当n/m或n/p较大时，广泛使用的Ho-Kalman算法和多变量输出误差状态空间（MOESP）算法对于MIMO系统是病态的。此外，通过分析Craḿer-Rao界，我们推导出了识别MIMO系统真实且稳定（或弱稳定）极点的基本极限，并证明了任何无偏极点估计算法达到一定精度水平的样本复杂度相对于n/(pm)呈超多项式爆炸式增长。提供了数值结果以说明Ho-Kalman算法和MOESP算法的病态性以及识别的基本极限。
This paper is concerned with the finite sample identification performance of an n dimensional discrete-time Multiple-Input Multiple-Output (MIMO) Linear Time-Invariant system, with p inputs and m outputs. We prove that the widely-used Ho-Kalman algorithm and Multivariable Output Error State Space (MOESP) algorithm are ill-conditioned for MIMO systems when n/m or n/p is large. Moreover, by analyzing the Cra\'mer-Rao bound, we derive a fundamental limit for identifying the real and stable (or marginally stable) poles of MIMO system and prove that the sample complexity for any unbiased pole estimation algorithm to reach a certain level of accuracy explodes superpolynomially with respect to n/(pm). Numerical results are provided to illustrate the ill-conditionedness of Ho-Kalman algorithm and MOESP algorithm as well as the fundamental limit on identification.
- [21] arXiv:2411.13252 (替换) [中文pdf, pdf, html, 其他]
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标题： 非方非线性系统的统一性能控制具有放松的可控性标题： Unified Performance Control for Non-Square Nonlinear Systems with Relaxed Controllability评论： 9页，13图，已投稿至期刊主题： 系统与控制 (eess.SY)
在本文中，我们研究了在放松可控性条件下的非方严格反馈非线性系统统一预设性能跟踪问题。 通过使用巧妙的矩阵分解并引入一些可行的辅助矩阵，构建了一个比当前最新技术更通用的可控性条件，该条件可以应用于具有执行器故障和未知但时变控制增益的方和非方非线性系统。 将放松的可控性条件和统一性能规范纳入反步设计过程，开发了一种预设性能容错控制器，可以在不修改控制器结构的情况下实现不同的性能需求，这更具灵活性和实用性。此外，通过将状态依赖不确定性中的可用核心信息嵌入设计过程，避免了未知可控性辅助矩阵和未知非线性性对系统稳定性的影响。 理论分析和数值仿真证明了所提出方法的有效性和优势。
In this paper, we investigate the problem of unified prescribed performance tracking for a class of non-square strict-feedback nonlinear systems under relaxed controllability conditions. By using a skillful matrix decomposition and introducing some feasible auxiliary matrices, a more generalized controllability condition than the current state of the art is constructed, which can be applied to both square and non-square nonlinear systems subject to actuator faults and unknown yet time-varying control gain. Incorporating the relaxed controllability conditions and the uniform performance specifications into the backstepping design procedure, a prescribed performance fault-tolerant controller is developed that can achieve different performance demands without modifying the controller structure, which is more flexible and practical.In addition, the destruction of the system stability by unknown controllability auxiliary matrices and unknown nonlinearities is circumvented by embedding the available core information of the state-dependent uncertainties into the design procedure. Both theoretical analysis and numerical simulation demonstrate the effectiveness and benefits of the proposed method.
- [22] arXiv:2502.07470 (替换) [中文pdf, pdf, html, 其他]
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标题： 基于辅助层的事件触发弹性一致性标题： On Event-Triggered Resilient Consensus Using Auxiliary Layer主题： 系统与控制 (eess.SY)
由于其设计简单，基于辅助层的弹性控制在文献中被广泛讨论，以减轻虚假数据注入（FDI）攻击的影响。 然而，由于连接额外层的附加通信链路而增加的通信负担在文献中经常被忽视。 本文通过考虑物理层（包含实际代理）和辅助层（包含虚拟代理）之间用于多代理系统弹性状态共识的层间通信的事件触发方法来弥补这一差距。 我们提供了基于状态和动态的事件触发机制，前者是后者的原因。 通过证明正的最小事件间时间（MIET）来建立排除Zeno行为。 提供了广泛的仿真和实验结果来说明所提出的方法。
Due to its design simplicity, auxiliary layer-based resilient control is widely discussed in the literature to mitigate the effects of False Data Injection (FDI) attacks. However, the increased communication burden due to additional communication links for connecting an extra layer is often overlooked in the literature. This paper bridges this gap by considering an event-triggered approach for inter-layer communication between the physical layer (containing actual agents) and the auxiliary layer (containing virtual agents) for the resilient state consensus in a multi-agent system. We provide state-based and dynamic event-triggering mechanisms, the former being the motivation for the latter. The exclusion of Zeno behavior is established by proving positive minimum inter-event time (MIET). Extensive simulation and experimental results are provided to illustrate the proposed methodology.
- [23] arXiv:2503.14986 (替换) [中文pdf, pdf, 其他]
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标题： 通过利用起动/发电机信号提高全电辅助动力装置（APU）燃气发生器的故障检测与隔离能力标题： Enhancing Fault Detection and Isolation in an All-Electric Auxiliary Power Unit (APU) Gas Generator by Utilizing Starter/Generator Signal期刊参考： 航空航天2025，12(7)，607主题： 系统与控制 (eess.SY)
本研究提出了一种新范式，通过利用起动/发电机的轴功率信息来增强全电辅助动力装置（APU）中气体发生器的故障检测与隔离（FDI）。首先，我们对APU电气化带来的FDI挑战和机遇进行了开创性研究。我们的分析表明，APU的电气化为利用起动/发电机的轴功率估计值来提高气体发生器FDI提供了新的可能性。随后，我们提供了全面的理论和分析证据，说明为什么、如何以及在多大程度上，起动/发电机的轴功率信息可以从根本上提高系统状态和气体发生器健康参数的估计精度，同时识别影响FDI性能改进的关键因素。所提出的范式的有效性和理论基础通过广泛的蒙特卡洛仿真得到了验证。此外，通过与最先进的气体发生器故障诊断方法进行综合比较分析，我们的实验结果不仅展示了所提出方法的优越性能，还验证了通过结合轴功率信息可以显著提升现有先进FDI技术的诊断能力。观察到的性能提升模式与我们的理论分析高度一致，验证了我们理论框架的有效性和指导意义。这些研究成果为回答三个基本问题提供了独特的视角：为什么需要联合诊断起动/发电机和气体发生器，如何实现，以及哪些因素决定了其有效性，从而为全电APU系统的FDI技术开辟了有前景的新途径。
This study proposes a novel paradigm for enhancing fault detection and isolation (FDI) of gas generators in all-electric auxiliary power unit (APU) by utilizing shaft power information from the starter/generator. First, we conduct a pioneering investigation into the challenges and opportunities for FDI brought about by APU electrification. Our analysis reveals that the electrification of APU opens up new possibilities for utilizing shaft power estimates from starter/generator to improve gas generator FDI. We then provide comprehensive theoretical and analytical evidence demonstrating why, how, and to what extent, the shaft power information from the starter/generator can fundamentally enhance the estimation accuracy of system states and health parameters of the gas generator, while also identifying the key factors influencing these improvements in FDI performance. The effectiveness of the proposed paradigm and its theoretical foundations are validated through extensive Monte Carlo simulations. Furthermore, through comprehensive comparative analysis with state-of-the-art gas generator fault diagnosis methods, our experimental results not only demonstrate the superior performance of the proposed approach but also validate that the diagnostic capabilities of existing advanced FDI techniques can be substantially enhanced by incorporating shaft power information. And the observed performance improvement patterns strongly align with our theoretical analysis, verifying both the effectiveness and guiding significance of our theoretical framework. These research findings provide a unique perspective in answering three fundamental questions: why joint fault diagnosis of the starter/generator and gas generator is essential, how it can be implemented, and what factors determine its effectiveness, thereby opening up promising new avenues for FDI technologies in all-electric APU systems.
- [24] arXiv:2504.01109 (替换) [中文pdf, pdf, html, 其他]
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标题： 不可压缩最优传输及其在流体混合中的应用标题： Incompressible Optimal Transport and Applications in Fluid Mixing评论： 8页。扩展版本（包含证明）主题： 系统与控制 (eess.SY) ; 数学物理 (math-ph) ; 优化与控制 (math.OC)
不可压缩流体混合的问题在许多工程应用中出现，并且多年来已经得到了充分的研究，但仍然存在许多未解的问题。 本文旨在解决这样一个问题：“高效的混合流场看起来是什么样的，它们是如何行为的？” 我们通过开发一个框架来解决这个问题，该框架受到最优质量传输的动力学和几何方法的启发。 具体来说，我们将流体混合问题表述为一个最优控制问题，其中动力学由连续性方程和不可压缩性约束给出。 我们证明，在这个框架下，可达的流体配置集可以形式上赋予无限维黎曼流形的结构，该结构由控制努力所诱导，而混合效率最高的流场则对应于这个黎曼空间中的测地线。
The problem of incompressible fluid mixing arises in numerous engineering applications and has been well-studied over the years, yet many open questions remain. This paper aims to address the question "what do efficient flow fields for mixing look like, and how do they behave?" We approach this question by developing a framework which is inspired by the dynamic and geometric approach to optimal mass transport. Specifically, we formulate the fluid mixing problem as an optimal control problem where the dynamics are given by the continuity equation together with an incompressibility constraint. We show that within this framework, the set of reachable fluid configurations can formally be endowed with the structure of an infinite-dimensional Riemannian manifold, with a metric which is induced by the control effort, and that flow fields which are maximally efficient at mixing correspond to geodesics in this Riemannian space.
- [25] arXiv:2409.07563 (替换) [中文pdf, pdf, html, 其他]
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标题： MPPI-通用：一种用于随机轨迹优化的CUDA库标题： MPPI-Generic: A CUDA Library for Stochastic Trajectory Optimization评论： 添加了缺失的致谢部分主题： 数学软件 (cs.MS) ; 分布式、并行与集群计算 (cs.DC) ; 机器人技术 (cs.RO) ; 系统与控制 (eess.SY)
本文介绍了一种新的C++/CUDA库，用于GPU加速的随机优化，称为MPPI-Generic。它提供了模型预测路径积分控制、管状模型预测路径积分控制和鲁棒模型预测路径积分控制的实现，并允许这些算法在许多现有的动力学模型和成本函数中使用。此外，研究人员可以按照我们的API定义创建自己的动力学模型或成本函数，而无需更改实际的模型预测路径积分控制代码。最后，我们在各种GPU上与其他流行的模型预测路径积分控制实现进行了计算性能比较，以展示我们的库所能提供的实时能力。库代码可在以下网址找到：https://acdslab.github.io/mppi-generic-website/ 。
This paper introduces a new C++/CUDA library for GPU-accelerated stochastic optimization called MPPI-Generic. It provides implementations of Model Predictive Path Integral control, Tube-Model Predictive Path Integral Control, and Robust Model Predictive Path Integral Control, and allows for these algorithms to be used across many pre-existing dynamics models and cost functions. Furthermore, researchers can create their own dynamics models or cost functions following our API definitions without needing to change the actual Model Predictive Path Integral Control code. Finally, we compare computational performance to other popular implementations of Model Predictive Path Integral Control over a variety of GPUs to show the real-time capabilities our library can allow for. Library code can be found at: https://acdslab.github.io/mppi-generic-website/ .
- [26] arXiv:2503.20839 (替换) [中文pdf, pdf, html, 其他]
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标题： TAR：通过对比学习实现的四足运动教师对齐表示标题： TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion评论： 这项工作已被接受在IEEE/RSJ智能机器人与系统国际会议（IROS）2025上发表。主题： 机器人技术 (cs.RO) ; 机器学习 (cs.LG) ; 系统与控制 (eess.SY)
四足运动通过强化学习（RL）通常使用教师-学生范式来解决，其中特权教师指导本体感觉学生策略。 然而，诸如特权教师与仅本体感觉的学生之间的表示不匹配、由于行为克隆导致的协变量偏移以及缺乏可部署的适应性等问题，导致在现实场景中泛化能力较差。 我们提出了通过对比学习对齐教师表示（TAR），一种利用特权信息和自监督对比学习来弥合这一差距的框架。 通过在模拟中使用对比目标将表示对齐到特权教师，我们的学生策略学习到结构化的潜在空间，并在分布外（OOD）场景中表现出稳健的泛化能力，超过了完全特权的“教师”。 结果表明，与最先进的基线相比，训练速度提高了2倍，以达到最佳性能。 与现有方法相比，OOD场景的泛化能力平均提高了40%。 此外，TAR在部署期间无缝过渡到学习，而无需特权状态，为样本高效、自适应的运动设定了新基准，并在现实场景中实现了持续微调。 开源代码和视频可在 https://amrmousa.com/TARLoco/ 获取。
Quadrupedal locomotion via Reinforcement Learning (RL) is commonly addressed using the teacher-student paradigm, where a privileged teacher guides a proprioceptive student policy. However, key challenges such as representation misalignment between privileged teacher and proprioceptive-only student, covariate shift due to behavioral cloning, and lack of deployable adaptation; lead to poor generalization in real-world scenarios. We propose Teacher-Aligned Representations via Contrastive Learning (TAR), a framework that leverages privileged information with self-supervised contrastive learning to bridge this gap. By aligning representations to a privileged teacher in simulation via contrastive objectives, our student policy learns structured latent spaces and exhibits robust generalization to Out-of-Distribution (OOD) scenarios, surpassing the fully privileged "Teacher". Results showed accelerated training by 2x compared to state-of-the-art baselines to achieve peak performance. OOD scenarios showed better generalization by 40% on average compared to existing methods. Moreover, TAR transitions seamlessly into learning during deployment without requiring privileged states, setting a new benchmark in sample-efficient, adaptive locomotion and enabling continual fine-tuning in real-world scenarios. Open-source code and videos are available at https://amrmousa.com/TARLoco/.
- [27] arXiv:2505.14647 (替换) [中文pdf, pdf, html, 其他]
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标题： 用于带线搜索的双层优化的顺序QCQP标题： Sequential QCQP for Bilevel Optimization with Line Search评论： IEEE 控制系统Letters（L-CSS）和IEEE决策与控制会议（CDC）2025主题： 优化与控制 (math.OC) ; 机器学习 (cs.LG) ; 系统与控制 (eess.SY)
双层优化涉及一个层次结构，其中一个问题嵌套在另一个问题中，导致各层次之间复杂的相互依赖关系。 我们提出了一种单循环、无需调整参数的算法，该算法保证随时可行性，即近似满足下层最优条件，同时确保上层目标函数的下降。 在每次迭代中，一个具有显式解的凸二次约束二次规划（QCQP）产生搜索方向，随后采用受控制屏障函数启发的回溯线搜索，以确保安全且统一正的步长。 所提出的算法具有可扩展性，无需超参数调整，并在较弱的局部正规性假设下收敛。 我们建立了基于一阶平稳性度量的O(1/k)遍历收敛速率，并在典型的双层任务中验证了该算法的有效性。
Bilevel optimization involves a hierarchical structure where one problem is nested within another, leading to complex interdependencies between levels. We propose a single-loop, tuning-free algorithm that guarantees anytime feasibility, i.e., approximate satisfaction of the lower-level optimality condition, while ensuring descent of the upper-level objective. At each iteration, a convex quadratically-constrained quadratic program (QCQP) with a closed-form solution yields the search direction, followed by a backtracking line search inspired by control barrier functions to ensure safe, uniformly positive step sizes. The resulting method is scalable, requires no hyperparameter tuning, and converges under mild local regularity assumptions. We establish an O(1/k) ergodic convergence rate in terms of a first-order stationary metric and demonstrate the algorithm's effectiveness on representative bilevel tasks.
- [28] arXiv:2507.06535 (替换) [中文pdf, pdf, 其他]
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标题： 基于图对比学习和标签再平衡的 AMS 电路可迁移寄生估计标题： Transferable Parasitic Estimation via Graph Contrastive Learning and Label Rebalancing in AMS Circuits评论： 最终版本被2025年国际计算机辅助设计会议（ICCAD）接受主题： 机器学习 (cs.LG) ; 系统与控制 (eess.SY)
图表示学习在模拟-混合信号（AMS）电路中对于各种下游任务至关重要，例如寄生参数估计。 然而，设计数据的稀缺性、标签的不平衡分布以及电路实现的固有多样性给学习鲁棒且可迁移的电路表示带来了重大挑战。 为解决这些限制，我们提出了CircuitGCL，一种新颖的图对比学习框架，该框架结合了表示扩散和标签再平衡，以增强跨异构电路图的可迁移性。 CircuitGCL采用自监督策略，通过超球面表示扩散学习拓扑不变的节点嵌入，消除了对大规模数据的依赖。 同时，引入了平衡均方误差（BMSE）和平衡softmax交叉熵（BSCE）损失，以缓解电路之间的标签分布差异，从而实现鲁棒且可迁移的寄生参数估计。 在TSMC 28nm AMS设计中的寄生电容估计（边级任务）和地电容分类（节点级任务）上进行评估，CircuitGCL优于所有最先进的（SOTA）方法，边回归的$R^2$改进为$33.64\% \sim 44.20\%$，节点分类的F1分数提升了$0.9\times \sim 2.1\times$。 我们的代码可在https://github.com/ShenShan123/CircuitGCL获取。
Graph representation learning on Analog-Mixed Signal (AMS) circuits is crucial for various downstream tasks, e.g., parasitic estimation. However, the scarcity of design data, the unbalanced distribution of labels, and the inherent diversity of circuit implementations pose significant challenges to learning robust and transferable circuit representations. To address these limitations, we propose CircuitGCL, a novel graph contrastive learning framework that integrates representation scattering and label rebalancing to enhance transferability across heterogeneous circuit graphs. CircuitGCL employs a self-supervised strategy to learn topology-invariant node embeddings through hyperspherical representation scattering, eliminating dependency on large-scale data. Simultaneously, balanced mean squared error (BMSE) and balanced softmax cross-entropy (BSCE) losses are introduced to mitigate label distribution disparities between circuits, enabling robust and transferable parasitic estimation. Evaluated on parasitic capacitance estimation (edge-level task) and ground capacitance classification (node-level task) across TSMC 28nm AMS designs, CircuitGCL outperforms all state-of-the-art (SOTA) methods, with the $R^2$ improvement of $33.64\% \sim 44.20\%$ for edge regression and F1-score gain of $0.9\times \sim 2.1\times$ for node classification. Our code is available at https://github.com/ShenShan123/CircuitGCL.
- [29] arXiv:2508.09010 (替换) [中文pdf, pdf, html, 其他]
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标题： 无优化快速最优控制：突变-滑行特性、单调性及在快速电池充电中的应用标题： Optimization-Free Fast Optimal Control: Bang-Ride Property, Monotonicity, and Applications to Fast Battery Charging主题： 优化与控制 (math.OC) ; 系统与控制 (eess.SY)
单输入快速最优控制问题，旨在尽可能快地实现最优目标，在各种现实应用中都会出现。 在快速电池充电的情况下，当使用详细的电池模型时，相关的最优控制问题会变得计算上具有挑战性。 一种最近的无启发式优化算法可以显著降低计算成本，并提供一个近似解，与实践中许多启发式输入配置一致。 这些启发式解有几个特殊性质：它们遵循一种“bang-ride”模式，始终激活一个约束并应用最大可行输入。 这项工作研究了上述性质何时出现在最优输入中，以及最终启发式输入配置何时满足必要最优性条件。 通过利用庞特里亚金最大原理（PMP），我们证明在约束切换和系统局部可控性的常规条件下，最优控制是“bang-ride”形式。 此外，在系统、目标函数和约束的限制灵敏度单调的情况下，特定类型的“bang-ride”行为，即应用最大可行输入，会出现。 这些结果为一类充电启发式方法和快速无优化算法提供了理论依据。
Single-input fast optimal control problems, which aim to achieve the optimal objective as fast as possible, occur in various real-world applications. In the case of fast battery charging, the associated optimal control problem becomes computationally challenging when detailed battery models are used. A recent heuristic optimization-free algorithm can significantly reduce the computational cost and provide an approximate solution, consistent with many heuristic input profiles in practice. These heuristic solutions have several special properties: They follow a bang-ride pattern that always activates a constraint and applies the maximum feasible input. This work investigates when the above properties arise in the optimal input, and ultimately, when the heuristic input profiles satisfy necessary optimality conditions. By exploiting Pontryagin's maximum principle (PMP), we show that the optimal control is bang-ride under regularity conditions on constraint switching and local controllability of the system. Moreover, the special type of bang-ride behavior, i.e., applying the maximum feasible input, arises under the monotonicity of the system, objective function, and restricted sensitivity of the constraints. These results provide a theoretical justification for a class of charging heuristics and the fast optimization-free algorithm.