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On the rate of convergence of distributed relaxed-ADMM algorithms in distributed optimization 分布式松弛admm算法在分布式优化中的收敛速度

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.066

Leonardo Armijos-Bacuilima , A. Mohamed Messilem , Luca Schenato , Ruggero Carli

In this paper, we present a preliminary analysis of the convergence rate of the relaxed ADMM-based algorithm recently introduced in the literature for distributed optimization problems. It is well known that the relaxed ADMM is a more general algorithm than the classical ADMM. Indeed, while the performance of the latter typically depends on one parameter, say p, the performance of the former depends on two parameters, say p and a, and the relaxed ADMM reduces to the classical ADMM when α = 1/2. Interestingly, from a computational point of view, the presence of the additional parameter a does not significantly complicate the updating steps of the algorithm. Nevertheless, the relaxed ADMM is much less considered in distributed optimization problems than the classical ADMM. In this paper, we show that by restricting to quadratic functions with the same convexity and to communication graphs that are regular connected graphs, it is possible to analytically compute the eigenvalues of the matrix that governs the dynamics of the algorithm based on the relaxed ADMM. Based on these eigenvalues, it is possible to design an efficient numerical procedure to evaluate the rate of convergence and to optimise it with respect to both α and p. Our results show that, by properly tuning the a parameter, the relaxed ADMM can can achieve superior convergence rates compared to its classical ADMM counterpart; in particular, for the family of graphs we considered, the optimal a typically tends to 1 as the number of agents increases. The analysis we present is preliminary, but suggests that the use of the relaxed ADMM can significantly improve the performance of the classical ADMM when applied to distributed optimization problems, at the price of a slight increase in computational complexity.

在本文中，我们对文献中最近提出的基于松弛admm的分布式优化算法的收敛速度进行了初步分析。众所周知，与经典ADMM相比，松弛ADMM是一种更通用的算法。事实上，后者的性能通常取决于一个参数，比如p，而前者的性能取决于两个参数，比如p和a，当α = 1/2时，松弛ADMM减少到经典ADMM。有趣的是，从计算的角度来看，额外参数a的存在并没有显著地使算法的更新步骤复杂化。然而，在分布式优化问题中，与经典ADMM相比，松弛ADMM较少被考虑。在本文中，我们证明了通过限制具有相同凸度的二次函数和正则连通图的通信图，可以解析地计算基于松弛ADMM的控制算法动力学的矩阵的特征值。基于这些特征值，可以设计一个有效的数值程序来评估收敛速度并对α和p进行优化。我们的结果表明，通过适当调整a参数，与经典ADMM相比，松弛ADMM可以获得更好的收敛速度；特别是，对于我们考虑的图族，随着代理数量的增加，最优a通常趋向于1。我们提出的分析是初步的，但表明，当应用于分布式优化问题时，使用宽松ADMM可以显着提高经典ADMM的性能，但代价是计算复杂性略有增加。

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引用次数: 0

Distributed Constraint-Coupled Optimization: Harnessing ADMM-consensus for robustness 分布式约束耦合优化：利用admm一致性实现鲁棒性

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.067

A. Mohamed Messilem , Guido Carnevale , Ruggero Carli

In this paper, we consider a network of agents that jointly aim to minimize the sum of local functions subject to coupling constraints involving all local variables. To solve this problem, we propose a novel solution based on a primal-dual architecture. The algorithm is derived starting from an alternative definition of the Lagrangian function, and its convergence to the optimal solution is proved using recent advanced results in the theory of timescale separation in nonlinear systems. The rate of convergence is shown to be linear under standard assumptions on the local cost functions. Interestingly, the algorithm is amenable to a direct implementation to deal with asynchronous communication scenarios that may be corrupted by other non-idealities such as packet loss. We numerically test the validity of our approach on a real-world application related to the provision of ancillary services in three-phase low-voltage microgrids.

在本文中，我们考虑了一个智能体网络，其共同目标是最小化受耦合约束的局部函数的总和，这些约束涉及所有局部变量。为了解决这个问题，我们提出了一种基于原始对偶结构的新解决方案。该算法从拉格朗日函数的另一种定义出发，并利用非线性系统时间尺度分离理论的最新成果证明了该算法收敛于最优解。在局部代价函数的标准假设下，收敛速度是线性的。有趣的是，该算法可以直接实现，以处理可能被其他非理想情况（如数据包丢失）破坏的异步通信场景。我们在与三相低压微电网提供辅助服务相关的实际应用中对我们方法的有效性进行了数值测试。

引用次数: 0

Multilayer Information Spillover Networks: Application to Stocks Cross-listed in Mainland China and Hong Kong 多层信息溢出网络：在内地与香港交叉上市公司中的应用

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.069

Hongyu Liu , Hao Xia

This study constructs a multilayer network framework that captures both within and across market interactions, to measure the volatility spillovers among stocks listed across both Hong Kong and Mainland Chinese exchanges. We find that: (i) From a system-level perspective, the volatility network’s total connectedness reacts notably to major events, while the leader for cross-market spillovers is not constant but shifts with time. (ii) From an individual-level perspective, cross-listed stock pairs display varying spillover influence across and within markets; however, the bidirectional spillovers between them are generally symmetric and tend to be strengthened by the introduction of inter-market connectivity policies. This research offers an innovative approach for a deeper understanding of the connectedness characteristics of cross-listed stocks and for optimizing risk management decisions.

本研究构建了一个多层网络框架，捕捉市场内部和市场之间的相互作用，以衡量在香港和中国内地交易所上市的股票之间的波动溢出效应。我们发现：(1)从系统层面来看，波动网络的总连通性对重大事件的反应显著，而跨市场溢出的领导者不是恒定的，而是随着时间的推移而变化。㈡从个人层面看，交叉上市的股票对在市场之间和市场内部表现出不同的溢出影响；然而，它们之间的双向溢出通常是对称的，并倾向于通过引入市场间互联互通政策而得到加强。本研究为深入了解交叉上市股票的连通性特征和优化风险管理决策提供了一种创新的方法。

引用次数: 0

Distributed Parameter Estimation with Adversaries via Multi-Hop Relays⁎ 基于多跳中继的对手分布式参数估计

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.054

Liwei Yuan , Hideaki Ishii

We study resilient distributed parameter estimation in multi-agent systems where some agents may malfunction. The objective is for each nonfaulty agent to locally estimate its parameter while it may interact with adversaries. To this end, we develop an algorithm using multi-hop relaying to achieve the goal in multi-agent networks with directed topologies. With multi-hop relays, agents can access more information of remote agents even though they communicate with only direct neighbors. We characterize a necessary and sufficient graph condition for our algorithm to succeed, which is denoted by the notion of robust following graphs. We prove that our condition with multi-hop relays is more relaxed than the one with one-hop case, and hence, our approach can tolerate more adversaries in the same network when multi-hop relays are applied. Lastly, numerical examples verify the efficacy of our algorithm.

研究了多智能体系统中某些智能体可能出现故障的弹性分布参数估计问题。目标是让每个无故障代理在与对手交互时局部估计其参数。为此，我们开发了一种使用多跳中继的算法来实现有向拓扑的多智能体网络的目标。使用多跳中继，即使代理只与直接邻居通信，也可以访问远程代理的更多信息。我们刻画了算法成功的充分必要图条件，用鲁棒跟随图的概念来表示。我们证明了我们的多跳中继条件比单跳中继条件更宽松，因此，当应用多跳中继时，我们的方法可以在同一网络中容忍更多的对手。最后，通过数值算例验证了算法的有效性。

引用次数: 0

Trade-off in Quantization Between Data-driven Design and Control Inputs⁎ 数据驱动设计和控制输入之间的量化权衡

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.052

Iori Takaki , Ahmet Cetinkaya , Hideaki Ishii

In this paper, we consider a remote control problem based on data-driven control with an emphasis on communication constraints. Specifically, we propose a direct data-driven stabilization method with quantization in input and state data for unknown discrete-time linear systems. Moreover, the controller is designed taking account of the effects of quantization in the feedback data. Logarithmic type quantization is employed, and we show the inherent trade-off in the quantization coarseness for data-driven design and feedback control. We illustrate the effectiveness of the method through numerical simulations.

在本文中，我们考虑了一个基于数据驱动控制的远程控制问题，重点是通信约束。具体来说，我们提出了一种对未知离散线性系统的输入和状态数据进行量化的直接数据驱动镇定方法。此外，控制器的设计还考虑了反馈数据中量化的影响。采用对数型量化，并展示了数据驱动设计和反馈控制在量化粗度方面的内在权衡。通过数值模拟验证了该方法的有效性。

引用次数: 0

From Dissensus to Consensus: Bias-Controlled Transition in Nonlinear Opinion Dynamics⁎ 从异议到共识：非线性意见动力学中的偏见控制转变

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.059

Rajul Kumar, Ningshi Yao

We propose a novel bias-based consensus framework for nonlinear opinion dynamics. Due to the observable and malleable nature of bias in human-robot interactions, we utilize it as a control parameter to achieve consensus. First, we analyze the Lyapunov–Schmidt reduced system near equilibrium under small bias assumptions. Through constrained cusp bifurcation, we show that increasing individual biases beyond identified thresholds—and relative biases beyond saddle-node limit points ensures consensus with a unique stable equilibrium. For large biases, we conduct a global phase-plane analysis. By establishing strong monotonicity and applying the Poincaré–Bendixson theorem, we eliminate the possibility of limit cycles and guarantee consensus with a unique stable attractor as equilibrium. Finally, along with numerical simulations for the two-agent, two-option case, we show that the proposed bias control approach extends seamlessly to decentralized multi-agent opinion consensus.

我们提出了一种新的基于偏见的非线性意见动态共识框架。由于人机交互中偏差的可观察性和延展性，我们利用它作为控制参数来达成共识。首先，我们分析了在小偏置假设下Lyapunov-Schmidt约简系统的近平衡态。通过约束尖分岔，我们证明了增加个体偏差超过识别阈值，以及增加相对偏差超过鞍节点极限点，可以确保一致性与唯一的稳定平衡。对于较大的偏差，我们进行全局相平面分析。通过建立强单调性，应用poincar - bendixson定理，我们消除了极限环的可能性，并以一个唯一的稳定吸引子作为平衡点，保证了一致性。最后，通过对两智能体、两种选择情况的数值模拟，我们证明了所提出的偏差控制方法可以无缝地扩展到分散的多智能体意见共识。

引用次数: 0

Neural Network-based Stability Guarantee for Dissensus Opinion Behaviors on the Sphere⁎ 基于神经网络的球面上意见分歧行为稳定性保证[j]

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.044

Junkai Wang , Ziqiao Zhang , Fumin Zhang

In this paper, we develop a neural network-based method to study opinion behaviors under a covariance-based dissensus algorithm. Driven by this dissensus algorithm, the opinions are updated based on relative interactions and gradually converge to dissensus on the sphere. This proposed neural network-based method samples data and trains a neural network to ensure the Lyapunov conditions, which significantly simplifies the Lyapunov function design for stability analysis. The regions of attraction for different dissensus equilibria can also be estimated under opinion dynamics on a unit sphere by training a neural network to approximate the solution of Zubov’s equation. Simulations demonstrate the performance of the proposed method.

在本文中，我们开发了一种基于神经网络的方法来研究基于协方差的异议算法下的意见行为。在该算法的驱动下，意见根据相对交互作用进行更新，并逐渐收敛为领域内的意见分歧。本文提出的基于神经网络的方法对数据进行采样并训练神经网络以保证Lyapunov条件，从而大大简化了稳定性分析的Lyapunov函数设计。通过训练神经网络来逼近Zubov方程的解，也可以在单位球面上的意见动态下估计不同意见均衡的吸引区域。仿真结果验证了该方法的有效性。

引用次数: 0

Human Impact of Visual Detail in Vehicle Lane-Keeping System Communication 车道保持系统通信中视觉细节对人的影响

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.004

Yu Chul Lee , Junmin Wang

Visual displays are used in vehicle automation systems to communicate the vehicle’s perception of the surrounding environment to the driver and passengers. This visual communication may impact how humans interact with the vehicle during driving. Thus, careful design of vehicle automation system visual communication is important for vehicle-driver collaboration. However, there is a lack of systematic study on how the level of detail in vehicle automation visual communication affects human driver’s workload, engagement, and acceptance. This paper presents a pilot research aiming to assess the impact of visual communication level of detail in vehicle automation systems. Both objective evaluation and subjective evaluation are conducted with human driving data collected on a driving simulator. Experimental results show a good agreement between the proposed objective assessment and subjective assessment.

视觉显示器用于车辆自动化系统，将车辆对周围环境的感知传达给驾驶员和乘客。这种视觉交流可能会影响人类在驾驶过程中与车辆的互动。因此，精心设计车辆自动驾驶系统的视觉通信对车辆与驾驶员的协作至关重要。然而，关于车辆自动化视觉通信的细节水平如何影响人类驾驶员的工作量、参与度和接受度，目前还缺乏系统的研究。本文提出了一项试点研究，旨在评估细节视觉通信水平对车辆自动化系统的影响。利用在驾驶模拟器上采集的人类驾驶数据进行客观评价和主观评价。实验结果表明，所提出的客观评价与主观评价具有较好的一致性。

引用次数: 0

Mitigation Strategy for Navigation Errors in Strict Route Plans 严格路由规划中导航错误的缓解策略

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.023

Mohammad Almudhaf , Ümit Özgüner

Localization errors can be caused by unintentional malfunctions or intentional attacks on localization processes and sensory devices. These errors can occur at critical driving situations and at high speeds, resulting in false navigation for Autonomous Vehicles (AV). Those situations can be at a road fork that is branching to an adjacent parallel road or to a different level of a multi-level road. The AV can merge to the wrong road and continue following the original route plan until the correct current location is realized. This can lead to hazardous driving situations or deviations from a strict plan. This work considers special cases where the AV must adhere to a strictly specified route, i.e. large vehicles that must be driven on highways. An algorithm that we call Road-Class Aware Rerouting (RCAR) is developed to identify the point of deviation within a directed graph representing a road map and find the optimal way to return to the original route or reach the destination while maintaining road network constraints. Simulated examples are included to illustrate the proposed algorithm.

定位错误可能是由无意的故障或对定位过程和传感设备的故意攻击引起的。这些错误可能发生在关键的驾驶情况和高速行驶中，导致自动驾驶汽车（AV）的错误导航。这些情况可能发生在通往相邻平行道路的岔路口，或者是通往多层次道路的不同级别。自动驾驶汽车可以合并到错误的道路，并继续按照原路线规划，直到实现正确的当前位置。这可能会导致危险的驾驶情况或偏离严格的计划。这项工作考虑了自动驾驶汽车必须遵守严格规定路线的特殊情况，即必须在高速公路上行驶的大型车辆。我们开发了一种称为道路类别感知重路由（RCAR）的算法，用于识别代表路线图的有向图中的偏差点，并在保持路网约束的情况下找到返回原始路线或到达目的地的最佳方式。通过仿真算例说明了所提出的算法。

引用次数: 0

Real-time predictive modeling of flight delays using distributed systems and machine learning 利用分布式系统和机器学习对航班延误进行实时预测建模

Q3 Engineering

IFAC-PapersOnLine

Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.034

Z. Kowalczuk , J. Wszołek , J. Okuniewska

This study presents a real-time system for flight delay prediction using distributed systems and machine learning. By integrating flight data from ADS-B signals, METARs, and TAF weather reports, the system processes the data streams via a reactive architecture. The applied predictive models, including random forests and linear regression, were validated and evaluated for their accuracy and scalability. The developed system offers a practical solution for improving decision-making in air traffic management.

本研究提出了一个使用分布式系统和机器学习的实时航班延误预测系统。通过整合来自ADS-B信号、METARs和TAF天气报告的飞行数据，系统通过响应式架构处理数据流。应用的预测模型，包括随机森林和线性回归，验证和评估其准确性和可扩展性。该系统为改进空中交通管理决策提供了实用的解决方案。

引用次数: 0

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