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A Proximal ADMM-Based distributed Optimal Energy Management Approach for Smart Grid With Stochastic Wind Power

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第5期71卷 2157-2170页

作者： Zhou, Yuan Shi, Xinli Guo, Luyao Wen, Guanghui Cao, Jinde Southeast Univ Sch Cyber Sci Engn Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

In this paper, we address a novel and comprehensive social welfare maximization (SWM) problem for the optimal energy management in a smart grid. The objective is to maximize the total social welfare of dispatchable devices in the smart grid while satisfying certain constraints. Each device in the smart grid is required to meet its local power constraints, and the system as a whole maintains supply-demand balance, taking into account transmission losses and stochastic output power. To facilitate distributed algorithm design, we initially transform the SWM problem into an equivalent dual problem, which is a distributed composite optimization problem. Subsequently, a novel fully distributed proximal alternating direction method of multipliers (PADMM) is proposed, where each agent can autonomously select non-coordinated step size parameters based solely on local information, independent of other agents and the network structure. Detailed convergence analysis is provided, and a worst-case (O)(1/k) convergence rate is established in the non-ergodic sense. Finally, several numerical experiments are conducted to confirm the effectiveness of the proposed algorithm.

关键词： Convergence Energy management Generators Energy storage Wind power generation Smart grids distributed algorithms ADMM optimal energy management smart grids distributed algorithm convergence rate

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Tracking-Based distributed Equilibrium Seeking for Aggregative Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第9期69卷 6026-6041页

作者： Carnevale, Guido Fabiani, Filippo Fele, Filiberto Margellos, Kostas Notarstefano, Giuseppe Alma Mater Studiorum Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy IMT Sch Adv Studies Lucca I-55100 Lucca Italy Univ Seville Dept Syst Engn & Automat Seville 41004 Spain Univ Oxford Dept Engn Sci Oxford OX1 2JD England

We propose fully distributed algorithms for Nash equilibrium seeking in aggregative games over networks. We first consider the case where local constraints are present and we design an algorithm combining, for each agent, the projected pseudogradient descent and a tracking mechanism to locally reconstruct the aggregative variable. To handle coupling constraints arising in generalized settings, we propose another distributed algorithm based on a recently emerged augmented primal-dual scheme and two tracking mechanisms to reconstruct, for each agent, both the aggregative variable and the coupling constraint satisfaction. Leveraging tools from singular perturbations analysis, we prove linear convergence to the Nash equilibrium for both schemes. Finally, we run extensive numerical simulations to confirm the effectiveness of our methods and compare them with state-of-the-art distributed equilibrium-seeking algorithms.

关键词： Couplings Heuristic algorithms Games Convergence Iterative methods distributed algorithms Vectors game theory network analysis and control optimization algorithms

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distributed Adaptive Algorithm for Resource Allocation Problem Over Weight-Unbalanced Graphs

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 416-426页

作者： Lian, Mengke Guo, Zhenyuan Wen, Shiping Huang, Tingwen HNU Chongqing Res Inst Chongqing Peoples R China Hunan Univ Sch Math Changsha 410012 Peoples R China Univ Technol Sydney Australian AI Inst Fac Engn Informat Technol Ultimo NSW 2007 Australia Texas A&M Univ Qatar Sci Program Doha Qatar

The paper presents the theoretical results on the resource allocation problems with local feasible convex set constraints under strongly connected graphs, where the objective function is non-smooth. It is worth noting that the communication graphs are general and include the weight-unbalanced directed graphs. Based on projection method, a distributed continuous-time adaptive algorithm is designed to achieve the optimal allocation. Moreover, the finite-time projection scheme is adopted to estimate a positive right-eigenvector of the out-Laplacian matrix from any initial value, which is a good indication for the robustness of the distributed algorithm when the communication topology is attacked and changed at some point. By convex and non-smooth analysis, we proved that the output variable asymptotically converges to the optimal allocation. Besides, the exponential convergence of the algorithm is analyzed without considering the local convex constraints. Finally, two illustrative examples are performed to substantiate the theoretical results under weight-unbalanced communication graphs.

关键词： Resource management Linear programming distributed algorithms Optimization Adaptive algorithms Topology Heuristic algorithms Resource allocation continuous-time adaptive algorithm multi-agent systems finite-time projection weight-unbalanced graphs

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Quantized Zeroth-Order Gradient Tracking Algorithm for distributed Nonconvex Optimization Under Polyak-Lojasiewicz Condition

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第10期54卷 5746-5758页

作者： Xu, Lei Yi, Xinlei Deng, Chao Shi, Yang Chai, Tianyou Yang, Tao Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Victoria Dept Mech Engn Victoria BC V8W 2Y2 Canada MIT Lab Informat & Decis Syst Cambridge MA 02139 USA Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

This article focuses on distributed nonconvex optimization by exchanging information between agents to minimize the average of local nonconvex cost functions. The communication channel between agents is normally constrained by limited bandwidth, and the gradient information is typically unavailable. To overcome these limitations, we propose a quantized distributed zeroth-order algorithm, which integrates the deterministic gradient estimator, the standard uniform quantizer, and the distributed gradient tracking algorithm. We establish linear convergence to a global optimal point for the proposed algorithm by assuming Polyak-Lojasiewicz condition for the global cost function and smoothness condition for the local cost functions. Moreover, the proposed algorithm maintains linear convergence at low-data rates with a proper selection of algorithm parameters. Numerical simulations validate the theoretical results.

关键词： Cost function Standards Convergence Vectors Quantization (signal) distributed algorithms Deep learning Gradient tracking algorithm linear convergence nonconvex optimization uniform quantizer zeroth-order algorithm

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ACapDS: An Energy-Efficient and Fault-Tolerant distributed Capacitated Dominating Set Algorithm for Industrial IoT

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IEEE ACCESS 2024年 12卷 38816-38829页

作者： Arapoglu, Ozkan Cabuk, Umut Can Dagdeviren, Orhan Challenger, Moharram Ege Univ Int Comp Inst TR-35040 Izmir Turkiye San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA Ege Univ Dept Comp Engn TR-35040 Izmir Turkiye Univ Antwerp Dept Comp Sci B-2020 Antwerp Belgium Flanders Make Strateg Res Ctr B-3001 Leuven Belgium

Design and analysis of energy-efficient and fault-tolerant dominating set (DS) algorithms are vital tasks for Industrial Internet of Things (IIoT) scenarios, as operational efficiency is a key objective in most industries. Such algorithms must maintain topology control, clustering, routing, and data aggregation. An IIoT deployment is said to pose the self-stabilization property if it can always recover to a steady state in a bounded time without any interruption whenever it is started at an unstable state. Self-stabilization is a fruitful principle for building fault-tolerant IIoT deployments. This paper proposes a novel distributed fault-tolerant capacitated DS (capDS) algorithm for IIoT systems. The algorithm is the first self-stabilizing capDS approach to the best of our knowledge. Proofs concerning the asynchronous behaviors of the algorithm, as well as the self-stabilization feature with regard to convergence and closure properties, are provided. R2C1 Besides, our theoretical analysis showed that the algorithm's approximation ratio is 6 for IIoT setups implemented as unit disk graphs. Measurements made using our testbed of 40 IRIS motes and the extensive TOSSIM simulations revealed that the proposed algorithm is up to 55% better in terms of coefficient of variation, requires up to 61% fewer moves, causes up to 13% less data traffic, and consumes up to 14% less energy when compared to a randomized approach and a minimum ID priority-based approach.

关键词： Industrial Internet of Things (IIoT) minimum capacitated dominating set distributed algorithms self-stabilizing algorithms fault tolerance

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distributed Online Learning Algorithm for Noncooperative Games Over Unbalanced Digraphs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15846-15856页

作者： Deng, Zhenhua Zuo, Xiaolong Cent South Univ Sch Automat Changsha 410075 Peoples R China

This article investigates constrained online noncooperative games (NGs) of multiagent systems over unbalanced digraphs, where the cost functions of players are time-varying and are gradually revealed to corresponding players only after decisions are made. Moreover, in the problem, the players are subject to local convex set constraints and time-varying coupling nonlinear inequality constraints. To the best of our knowledge, no result about online games with unbalanced digraphs has been reported, let alone constrained online games. To seek the variational generalized Nash equilibrium (GNE) of the game online, a distributed learning algorithm is proposed based on gradient descent, projection, and primal-dual methods. Under the algorithm, sublinear dynamic regrets and constraint violations are established. Finally, online electricity market games illustrate the algorithm.

关键词： Heuristic algorithms Games Cost function Couplings distributed algorithms Costs Power system dynamics distributed algorithms generalized Nash equilibrium (GNE) multiagent systems noncooperative games (NGs) online games unbalanced digraphs

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CoLoSSI: Multi-Robot Task Allocation in Spatially-distributed and Communication Restricted Environments

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IEEE ACCESS 2024年 12卷 132838-132855页

作者： Ansari, Ishaq Mohammed, Abubakr Ansari, Yaqoob Ansari, Mohammed Yusuf Razak, Saquib Flushing, Eduardo Feo Texas A&M Univ Qatar Dept Elect & Comp Engn Ar Rayyan Qatar Carnegie Mellon Univ Qatar Dept Comp Sci Ar Rayyan Qatar Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Univ Michigan Dept Comp Sci & Engn Ann Arbor MI 48109 USA

In our research, we address the problem of coordination and planning in heterogeneous multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this problem has been framed as a task allocation problem that maps tasks to robots. However, all previous work assumes that tasks are atomic procedures. In this work, we relax this assumption and adopt a non-atomic model of tasks that enables robots to accomplish mission tasks incrementally over disjoint periods, precisely to account for the possibility of having a task serviced by numerous individual contributions over time. We propose a cooperative, load-balancing task allocation and scheduling algorithm based on sequential single-item auctions (CoLoSSI) that explicitly considers the non-atomicity of tasks, promotes synergies between agents, and enables cooperation while maintaining computational tractability. We also propose a fully distributed variant of CoLoSSI that tackles sparse, communication-restricted scenarios. Computational and simulation results confirm the efficacy of the proposed approaches for generating good-quality mission plans with low computational effort.

关键词： Task analysis Robots Robot kinematics Resource management Robot sensing systems Computational efficiency Heuristic algorithms Multi-robot systems Cooperative robotics distributed algorithms load balancing multi-robot systems non-atomic task model search and rescue robotics sequential single-item auctions task allocation

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distributed Algorithm for Continuous-Type Bayesian Nash Equilibrium in Subnetwork Zero-Sum Games

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第2期11卷 915-927页

作者： Zhang, Hanzheng Chen, Guanpu Hong, Yiguang Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100190 Peoples R China KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst S-11428 Stockholm Sweden Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201804 Peoples R China

In this article, we consider a continuous-type Bayesian Nash equilibrium (BNE) seeking problem in subnetwork zero-sum games, which is a generalization of either deterministic subnetwork zero-sum games or discrete-type Bayesian zero-sum games. In this model, because the feasible strategy set is composed of infinite-dimensional functions and is not compact, it is hard to seek a BNE in a noncompact set and convey such complex strategies in network communication. To this end, we give a two-step design. One is a discretization step, where we discretize continuous types and prove that the BNE of the discretized model is an approximate BNE of the continuous model with an explicit error bound. The other is a communication step, where we adopt a novel compression scheme with a designed sparsification rule and prove that agents can obtain unbiased estimations through the compressed communication. Based on the two steps, we propose a distributed communication-efficient algorithm to practically seek an approximate BNE, and further provide the convergence analysis and explicit error bounds.

关键词： Games Bayes methods Approximation algorithms distributed algorithms Convergence Nash equilibrium Control systems Bayesian game communication compression discretization distributed algorithm equilibrium approximation subnetwork game zero-sum game

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A data-driven distributed fault diagnosis scheme for large-scale systems based on correlation analysis

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IET CONTROL THEORY AND APPLICATIONS 2024年第2期18卷 201-212页

作者： Li, Zhennan Li, Linlin Ding, Steven X. Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst AKS Duisburg Germany

This paper studies data-driven distributed fault diagnosis for large-scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to improve the fault detection performance by minimizing the impact of noise-induced uncertainty. The core of the method is to implement the correlation of the coupled nodes to reduce the covariance of the residual signal in a distributed manner. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of abnormality. A case study on Tennessee Eastman process is given in the end to demonstrate the proposed approach. This paper studies data-driven distributed fault diagnosis for large-scale systems using sensor networks. To be specific, a distributed fault detection scheme based on correlation analysis is first proposed to mitigate the impact of noise-induced uncertainty on fault detection performance. Then, a fault localization approach is developed to locate the fault by measuring and comparing the degree of ***

关键词： distributed algorithms fault diagnosis fault location

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Lightweight distributed Gaussian Process Regression for Online Machine Learning

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第6期69卷 3928-3943页

作者： Yuan, Zhenyuan Zhu, Minghui Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA

In this article, we study the problem where a group of agents aims to collaboratively learn a common static latent function through streaming data. We propose alight weight distributed Gaussian process regression (GPR)algorithm that is cognizant of agents' limited capabilities in communication, computation, and memory. Each agent independently runs agent-based GPR using local streaming data to predict test points of interest;then, the agents collaboratively execute distributed GPR to obtain global predictions over a common sparse set of test points;finally, each agent fuses results from distributed GPR with agent-based GPR to refine its predictions. By quantifying the transient and steady-state performances in predictive variance and error, we show that limited inter agent communication improves learning performances in the sense of Pareto. Monte Carlo simulation is conducted to evaluate the developed algorithm.

关键词： Gaussian processes Prediction algorithms Kernel Training data Training Servers Approximation algorithms distributed algorithms machine learning

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