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distributed optimization for uncertain nonlinear MASs under event-triggered communication

AUTOMATICA 2025年 177卷

作者： Fan, Sha Yue, Dong Wang, Bohui Deng, Chao Yan, Huaicheng Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710049 Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

In this paper, we consider the distributed adaptive optimization problem for nonlinear multi-agent systems (MASs) with unmatched uncertainties under a dynamic event-triggered mechanism (DETM). Unlike the existing distributed optimization results that focus on the linear MASs under static or simple event-triggered communication, more general nonlinear MASs with unmatched uncertainties are considered in this paper, which makes the design of the distributed optimization strategy challenging. To solve this problem, a distributed adaptive optimization algorithm based on the dynamic eventtriggered mechanism is first proposed for first-order uncertain nonlinear MASs, which could provide a dynamic agent interaction-based adaptive event sampling. Based on this, a DETM-based distributed adaptive optimization algorithm is designed for high-order uncertain nonlinear MASs by employing the backstepping technique. Specifically, by introducing a high-order filter, an improved distributed optimization algorithm is further proposed, to ensure the existence of high-order derivatives of the local reference, making the application of the backstepping technique easy. Ultimately, a simulation example with comparisons is provided to show the efficacy of the developed algorithm. (c) 2025 Published by Elsevier Ltd.

关键词： distributed optimization Adaptive control Backstepping technique Event-triggered communication

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distributed optimization for the trade-off between state-of-charge balancing and strategic battery usage in a networked BESS

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JOURNAL OF ENERGY STORAGE 2025年 105卷

作者： Qiu, Chenyang Qian, Yangyang Lin, Zongli Shamash, Yacov A. Univ Virginia Charles L Brown Dept Elect & Comp Engn POB 400743 Charlottesville VA 22904 USA SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

Battery energy storage systems (BESSs) are critical components of microgrids. In this paper, we consider the dynamics of discharging batteries and propose a model for making a trade-off between balancing the state- of-charge (SoC) of battery units in a networked BESS and strategic usage of these battery units. To realize such a trade-off, we formulate an optimization problem that takes the total discharging power requirement and the limitations on individual battery units into account. The dynamics of SoC rely on the solution to the optimization problem. Furthermore, we reformulate the optimization problem in a distributed manner, employing estimators for certain global variables, and analyze the gap between the optimum with global information and the optimum with estimation in the steady state. Our simulation results verify that the trade-off between SoC balancing and strategic usage of battery units can be adjusted by tuning the weighting parameters in the optimization problems.

关键词： Microgrids Battery energy storage systems State-of-charge balancing distributed optimization

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distributed optimization of multi-agent systems with time-invariant and time-varying cost functions

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EUROPEAN JOURNAL OF CONTROL 2025年 83卷

作者： Weng, Kai Ren, Yan Zhao, Liyun Gao, Siyu Inner Mongolia Univ Sci & Technol Coll Mech Engn Baotou 014010 Inner Mongolia Peoples R China Inner Mongolia Univ Sci & Technol Coll Automat & Elect Engn Baotou 014010 Inner Mongolia Peoples R China Inner Mongolia Univ Sci & Technol Coll Sci Baotou 014010 Inner Mongolia Peoples R China

This study investigated the distributed optimization problem of multi-agent systems (MASs). The aim was to design optimization algorithms for two cases where the cost function was time-invariant and time-varying to allow the system to reach the global optimum. First, a zero-gradient-sum (ZGS) distributed optimization algorithm based on second-order Hessian matrix information was designed to solve the time-invariant cost function optimization problem and allow the system to converge to the optimal state quickly in a fixed-time. Second, considering the case of a time-varying cost function, a distributed optimization algorithm based on a prediction-correction structure was designed by improving the above algorithm. This algorithm allows a system to converge to an optimal trajectory within a fixed-time. Finally, simulations were conducted to demonstrate the effectiveness of the control algorithms.

关键词： Multi-agent systems distributed optimization Time-invariant cost function Time-varying cost function Hessian matrix

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distributed optimization of Resource Efficiency for Federated Edge Intelligence in AAV-Enabled IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 9449-9464页

作者： Li, Yingyu Rao, Jiangying Wang, Lian Xiao, Yong Ge, Xiaohu Shi, Guangming China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Pazhou Lab Huangpu Guangzhou 510555 Guangdong Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China

Autonomous aerial vehicles (AAV)-enabled IoT networks have shown promising potential in a range of novel applications and service scenarios, such as extending the network coverage, extending the battery lifetime of IoT networks, and also supporting temporary data collecting and processing needs in various emergency situations. This article studies a federated edge intelligence (FEI) network based on the data collected and uploaded by a AAV-enabled IoT network. More specifically, a set of AAVs has periodically collected and uploaded the data generated by an IoT network to a set of edge servers. Edge servers will then collaboratively construct shared models based on the uploaded datasets. The data uploading performance of a AAV-enabled IoT network and the computational capacity of edge servers are entangled with each other in influencing the overall model training process. We propose a new framework called AAV-enabled IoT network for FEI (U-FEI). This framework enables edge servers to assess how many data samples need to be collected based on the energy costs of the AAV-enabled IoT network. It also considers the local data processing capacity of the edge servers. As a result, the edge servers can request just the right amount of data from the AAVs, which is enough to train a satisfactory model. We evaluate the energy cost for data uploading of AAVs when the data can be uploaded from two different types of frequencies: 1) licensed bands (e.g., using 5G) and 2) unlicensed bands (e.g., using Wi-Fi, ZigBee, or 5G NR-U). We prove that the cost minimization problem of the entire AAV-enabled IoT network is separable and can be divided into a set of subproblems, each of which can be solved by an individual edge server. We also introduce a mapping function to quantify the computational load of edge servers under the combinations of three key parameters: 1) size of the dataset;2) local batch size;and 3) number of local training passes. Finally, we adopt an alternative direction

关键词： Internet of Things Servers Costs Edge computing Autonomous aerial vehicles Training Resource management optimization Data models Computational modeling 6G autonomous aerial vehicles (AAV) distributed optimization edge intelligence federated learning (FL) IoT

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distributed optimization of virtual storage plant aggregating large-scale storage for grid reserve provision: Experimental validations

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CONTROL ENGINEERING PRACTICE 2025年 157卷

作者： Wang, Xiao Krpan, Matej Kuzle, Igor Parisio, Alessandra Wuhan Univ Technol Dept Automat Wuhan Peoples R China Univ Zagreb Dept Energy & Power Syst Zagreb Croatia Univ Manchester Dept Elect & Elect Engn Manchester England

This paper investigates the participation in grid services of large-scale storage devices aggregated as a virtual storage plant (VSP). A distributed optimization framework is developed to enable highly autonomous operation of the VSP for simultaneous provisions of the reserve service and voltage regulation of the local distribution network. A hardware-in-the-loop (HIL) testbed with real storage assets and realistic communication environment is established based on a transnational cooperation across multiple laboratories. Implementation details are provided with an emphasis on the forced-synchronized communication. The experimental results demonstrate the benefits and limitations of such distributed coordination for geographically dispersed storage devices over the cyber-physical networks, where the collected data is used to provide domain knowledge to improve the control design. This obtains important insights on the implementations of similar schemes in future power networks.

关键词： distributed storage Virtual storage plant Hardware in the loop distributed optimization

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distributed optimization strategy for networked microgrids based on network partitioning

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APPLIED ENERGY 2025年 378卷

作者： Wang, Jingjing Yao, Liangzhong Liang, Jun Wang, Jun Cheng, Fan Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Cardiff Univ Sch Engn Cardiff CF24 3AA Wales

The integration of a large number of distributed resources into an active distribution network presents significant challenges, including high control dimensionality, strong output uncertainty, and low utilization of renewable energy. This paper introduces a distributed optimization strategy for networked microgrids based on network partitioning to alleviate the computational burden, reduce operating costs, and enhance the utilization of renewable energy. The active distribution network is partitioned into networked microgrids, and a two-layer distributed optimization model is developed for their management. The first layer focuses on intra-day distributed optimal dispatch, balancing power and load by managing various flexible resources and the exchange power between virtual microgrids. The second layer, real-time distributed power tracking optimization, coordinates flexible resources within virtual microgrids to mitigate photovoltaic power fluctuations and track intra-day dispatch instructions. Simulation results demonstrate that the proposed network partitioning method reduces dispatch costs by 5.3 % and increases the utilization of distributed PV by 3 %, compared to the NP method that only considering modularity. Moreover, calculation times for intra-day dispatch and real-time power tracking are reduced by approximately 26 % and 50 %, respectively, compared to centralized control.

关键词： High penetration photovoltaic integration Network partitioning Networked microgrids distributed optimization

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distributed optimization algorithm for multi-agent networks with lazy gradient information

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ASIAN JOURNAL OF CONTROL 2025年第1期27卷 532-539页

作者： Mo, Lipo Yang, Yang Huang, Xiankai Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China Beijing Technol & Business Univ Sch Comp & Artificial Intelligence Beijing Peoples R China

Based on the so-called lazy gradient information, this note proposes two communication-reduced distributed optimization algorithms over undirected multi-agent networks. The lazy gradients refer to some gradients that do not change much in the past iterations and thus may not be distributed among agents which correspondingly reduces the communication load in the networks. For both the deterministic and the stochastic frameworks, the asymptotic properties of the distributed optimization algorithms are established. Compared with the existing literature using the lazy gradient information, the proposed algorithms in the paper are fully distributed and more suitable for the situation of decentralized multi-agent networks. The effectiveness of the proposed algorithms is also testified through numerical simulations.

关键词： communication reduction distributed optimization gradient descent algorithm lazy gradient

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distributed optimization for penalized regression in massive compositional data

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APPLIED MATHEMATICAL MODELLING 2025年 141卷

作者： Chao, Yue Huang, Lei Ma, Xuejun Soochow Univ Sch Math Sci Dept Stat Suzhou Peoples R China Xiamen Univ MOE Key Lab Econometr WISE Xiamen Peoples R China Southwest Jiaotong Univ Sch Math Dept Stat Chengdu Peoples R China

Compositional data have been widely used in various fields to analyze parts of a whole, providing insights into proportional relationships. With the increasing availability of extraordinarily large compositional datasets, addressing the challenges of distributed statistical methodologies and computations has become essential in the era of big data. This paper focuses on the optimization methodology and practical application of the distributed sparse penalized linear log- contrast model for massive compositional data, specifically in the context of medical insurance reimbursement ratio prediction. We propose two distributed optimization techniques tailored for centralized and decentralized topologies to effectively tackle the constrained convex optimization problems that arise in this application. Our algorithms are rooted in the frameworks of the alternating direction method of multipliers and the coordinate descent method of multipliers, making them available for distributed data scenarios. Notably, in the decentralized topology, we introduce a distributed coordinate-wise descent algorithm that employs a group alternating direction method of multipliers to achieve efficient distributed regularized estimation. We rigorously present convergence analysis for our decentralized algorithm, ensuring its reliability for practical applications. Through numerical experiments on both simulated datasets and a real- world medical insurance dataset, we evaluate the performance of our proposed algorithms.

关键词： Massive compositional data distributed optimization Augmented Lagrangian Coordinate-wise descent Variable selection Medical insurance

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distributed optimization on Matrix-Weighted Networks

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第1期46卷 367-380页

作者： Cui, Qiuyan Ji, Zhijian Liu, Yungang Lin, Chong Qingdao Univ Inst Complex Sci Coll Automat Shandong Key Lab Ind Control Technol Qingdao Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

This article is intended to tackle the optimization problems for continuous-time first-order and second-order multi-agent systems (MASs) operating over matrix-weighted networks. A matrix-weighted network serves as a powerful framework to model the interdependence among agents' multidimensional states, providing an effective approach to analyze smart grids, intelligent transportation systems, and so forth. Our optimization objective is to facilitate the convergence of all agents toward the optimal value of a global cost function, which is formed by a sum of local cost functions. To achieve this goal, distributed optimization algorithms based on Hessian matrix and gradient information are constructed. Additionally, an edge-based event-triggered mechanism is utilized to avoid communicating with all neighbors at the time of event triggering. It is proved that this mechanism theoretically excludes Zeno behavior. The results show that the proposed algorithms ensure that the agents can achieve the optimization goal while reducing energy consumption. Finally, an application is presented to substantiate the theoretical results.

关键词： continuous-time distributed optimization edge-based event-triggered control exponentially converge matrix-weighted networks

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distributed optimization With Asynchronous Computation and Event-Triggered Communication

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第2期70卷 1084-1099页

作者： Dong, Ziwei Jin, Yaochu Mao, Shuai Ren, Wei Du, Wei Tang, Yang East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Westlake Univ Sch Engn Trustworthy & Gen AI Lab Hangzhou 310030 Peoples R China Nantong Univ Sch Elect Engn & Automat Nantong 226019 Peoples R China Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

The implementation of distributed optimization, depending on the application, imposes escalating demands on communication and computational synchronization, with the general desire for the robust performance in the face of computationally slow agents and the avoidance of unnecessary communication. In this article, we propose a distributed algorithm with asynchronous computation and event-triggered communication (DAAET) that enables the nodes to flexibly determine their update and information transmission instants. DAAET achieves compatibility with nodes operating at varying computation frequencies and accomplishes a reduction in both wall time and communication costs. Meanwhile, this article proposes a model reconstruction technique to handle disconnectivity arising from the asynchronous implementation of the event-triggered mechanism. Theoretical analysis demonstrates the algorithm's linear convergence to the global optimum under relaxed conditions. The effectiveness and advantages of our approach are demonstrated through a set of examples, showcasing its potential for practical applications.

关键词： optimization Convergence Topology Heuristic algorithms Directed graphs Computational efficiency Upper bound Asynchronous scheme distributed optimization event-triggered scheme linear convergence rate

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