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distributed optimization in an Energy-Constrained Network: Analog Versus Digital Communication Schemes

IEEE TRANSACTIONS ON INFORMATION THEORY 2013年第3期59卷 1803-1817页

作者： Razavi, Alireza Zhang, Wenbo Luo, Zhi-Quan Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

We consider a distributed optimization problem whereby a network of n nodes, S-l, l is an element of {1, ... , n}, wishes to minimize a common strongly convex function f(x), x = [x(1), ... , x(n)](T), under the constraint that node S-l controls variable x(l) only. The nodes locally update their respective variables and periodically exchange their values with their neighbors over a set of predefined communication channels. Previous studies of this problem have focused mainly on the convergence issue and the analysis of convergence rate. In this study, we consider noisy communication channels and study the impact of communication energy on convergence. In particular, we study the minimum amount of communication energy required for nodes to obtain an epsilon-minimizer of f(x) in the mean square sense. For linear analog communication schemes, we prove that the communication energy to obtain an epsilon-minimizer of f(x) must grow at least at the rate of Omega(1/epsilon), and this bound is tight when is convex quadratic. Furthermore, we show that the same energy requirement can be reduced to O (log(2) 1/epsilon) if a suitable digital communication scheme is used.

关键词： Communication energy distributed optimization energy-constrained network

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distributed optimization Algorithms for Wide-Area Oscillation Monitoring in Power Systems Using Interregional PMU-PDC Architectures

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IEEE TRANSACTIONS ON SMART GRID 2015年第5期6卷 2529-2538页

作者： Nabavi, Seyedbehzad Zhang, Jianhua Chakrabortty, Aranya N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27606 USA

In this paper, we present a set of distributed algorithms for estimating the electro-mechanical oscillation modes of large power system networks using synchrophasors. With the number of phasor measurement units (PMUs) in the North American grid scaling up to the thousands, system operators are gradually inclining toward distributed cyber-physical architectures for executing wide-area monitoring and control operations. Traditional centralized approaches, in fact, are anticipated to become untenable soon due to various factors such as data volume, security, communication overhead, and failure to adhere to real-time deadlines. To address this challenge, we propose three different communication and computational architectures by which estimators located at the control centers of various utility companies can run local optimization algorithms using local PMU data, and thereafter communicate with other estimators to reach a global solution. Both synchronous and asynchronous communications are considered. Each architecture integrates a centralized Prony-based algorithm with several variants of alternating direction method of multipliers (ADMM). We discuss the relative advantages and bottlenecks of each architecture using simulations of IEEE 68-bus and IEEE 145-bus power system, as well as an Exo-GENI-based software defined network.

关键词： Alternating direction method of multipliers (ADMM) distributed optimization phasor measurement units (PMUs) Prony wide area oscillation monitoring

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distributed optimization for Scheduling Electrical Demand in Complex City Districts

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IEEE SYSTEMS JOURNAL 2018年第4期12卷 3226-3237页

作者： Diekerhof, Michael Schwarz, Sebastian Martin, Florian Monti, Antonello Rhein Westfal TH Aachen Inst Automat Complex Power Syst D-52062 Aachen Germany

This paper analyzes and compares one central and one distributed optimization method applied to complex city districts embedded in a hierarchical architecture. We define a complex city district by the coexistence of residential, nonresidential, and industry. The proposed methods are for offline, day-ahead optimization for operation planning purposes of, e.g., electrothermal heating systems. This paper analyzes how this way city districts may provide flexibility for demand response purposes. The optimization objective is the matching of supply and demand on system level by maximizing the usage of renewable energy generation. We provide quantitative and qualitative results to compare the performance of the algorithms and their potential for providing flexibility.

关键词： City districts demand response (DR) distributed optimization dual decomposition (DD) flexibility

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distributed optimization With Nonconvex Velocity Constraints, Nonuniform Position Constraints, and Nonuniform Stepsizes

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2019年第6期64卷 2575-2582页

作者： Lin, Peng Ren, Wei Yang, Chunhua Gui, Weihua Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

This note is devoted to the distributed optimization problem of multi-agent systems with nonconvex velocity constraints, nonuniform position constraints, and nonuniform stepsizes. Two distributed constrained algorithms with nonconvex velocity constraints and nonuniform stepsizes are proposed in the absence and the presence of nonuniform position constraints by introducing a switching mechanism to guarantee all agents' position states to remain in a bounded region. The algorithm gains need not to be predesigned and can be selected by each agent using its own and neighbors' information. By a model transformation, the original nonlinear time-varying system is converted into a linear time-varying one with a nonlinear error term. Based on the properties of stochastic matrices, it is shown that the optimization problem can be solved as long as the communication topologies are jointly strongly connected and balanced. One numerical example is given to show the obtained theoretical results.

关键词： distributed optimization nonconvex constraint sets nonuniform position constraint sets nonuniform step-sizes

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distributed optimization of high-order nonlinear multi-agent systems with disturbance under switching topologies

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第3期45卷 974-992页

作者： Zhu, Wenbo Wang, Qingling Southeast Univ Sch Automat Nanjing 210096 Peoples R China

This article investigates distributed optimization of high-order nonlinear multi-agent systems (MASs) with disturbance under switching topologies. First, we propose a class of new distributed control algorithms for high-order MASs under switching topologies, such that all agents asymptotically reach consensus at the unique minimizer of the sum of cost functions. Then, by constructing disturbance observer, the proposed algorithms are extended to address distributed optimization problems for high-order nonlinear MASs with disturbance under switching topologies. In particular, both of new presented methods do not require optimal reference signal generator, which can save the computational resources of systems. In final, simulations are conducted to validate the theoretical results. A class of distributed algorithms are proposed to solve distributed optimization problems for high-order nonlinear MASs with disturbance under UJSC digraphs. Typically different from the existing results for high-order nonlinear MASs, the new algorithms do not adopt embedded technique, i.e., the extra optimal reference signal generator is not ***

关键词： cooperative control distributed optimization high-order dynamics multi-agent systems switching digraphs

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distributed optimization Approach for Solving Continuous-Time Lyapunov Equations With Exponential Rate of Convergence

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第3期52卷 1684-1691页

作者： Zeng, Xianlin Chen, Jie Sun, Jian Hong, Yiguang Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Beijing Inst Technol Key Lab Biomimet Robots & Syst Minist Educ Beijing 100081 Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200092 Peoples R China Univ Chinese Acad Sci Acad Math & Syst Sci Chinese Acad Sci Key Lab Syst & Control Beijing 100190 Peoples R China

This article establishes an approach, based on distributed optimization, for solving continuous-time Lyapunov equations (CTLE) over multiagent networks. Each agent in the network knows partial information of the CTLE and has a dynamical system to estimate exact or least-squares solutions. The aim of agents is to find a solution to CTLE by sharing information with connected agents over a network. This article develops distributed algorithms with an exponential rate of convergence for CTLE via the convex optimization design. Finally, this article presents numerical simulations to show the efficacy of the main results.

关键词： optimization distributed algorithms Mathematical model Convergence Standards Multi-agent systems Matrix decomposition Continuous-time Lyapunov equation (CTLE) distributed optimization exponential convergence rate

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distributed optimization for a class of high-order nonlinear multiagent systems with unknown dynamics

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2018年第17期28卷 5545-5556页

作者： Tang, Yutao Beijing Univ Posts & Telecommun Sch Automat 10 Xitucheng Rd Beijing 100876 Peoples R China

In this paper, we study a distributed optimization problem for a class of high-order multiagent systems with unknown dynamics. In comparison with existing results for integrators or linear agents, we need to overcome the difficulties brought by the unknown nonlinearities and the optimization requirement. For this purpose, we employ an embedded control-based design and first convert this problem into an output stabilization problem. Then, two kinds of adaptive controllers are given for these agents to drive their outputs to the global optimal solution under some mild conditions. Finally, we show that the estimated parameter vector converges to the true parameter vector under some well-known persistence of excitation condition. The efficacy of these algorithms was verified by a simulation example.

关键词： adaptive control distributed optimization embedded design parameter convergence unknown dynamics

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distributed optimization for Multi-Agent Systems With Time Delay

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IEEE ACCESS 2020年 8卷 123019-123025页

作者： Yang, Zhengquan Pan, Xiaofang Zhang, Qing Chen, Zengqiang Civil Aviat Univ China Coll Sci Tianjin 300300 Peoples R China Nankai Univ China Coll Artificial Intelligence Tianjin 300350 Peoples R China

The distributed optimization for multi-agent systems with time delay and first-order is investigated in this paper. The objective of the distributed optimization is to optimize the objective function composed of the sum of local objective functions, which can only be known by its corresponding agents. Firstly, a distributed algorithm for time-delay systems is proposed to solve the optimization problem that each agent depends on its own state and the state between itself and its neighbors. Secondly, Lyapunov-Krasovskii function is used to prove that the states of each agent can be asymptotically the same, and the states are optimal. Finally, an example is given for illustrating the analytical results and a comparison is also gave to illustrate the differences between the algorithm of this paper and other results.

关键词： distributed optimization multi-agent systems time delay Lyapunov-Krasovskii function zero-gradient-sum algorithm

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distributed optimization of Nonlinear Multiagent Systems via Event-Triggered Communication

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第6期70卷 2092-2096页

作者： Liu, Dan Shen, Mouquan Jing, Yanhui Wang, Qing-Guo Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China Xinyang Normal Univ Sch Math & Stat Xinyang 464000 Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Zhuhai 519087 Peoples R China Beijing Normal Univ & Hong Kong Baptist Univ Unite Guangdong Prov Key Lab Interdisciplinary Res & App Zhuhai 519087 Peoples R China

This brief is concerned with a distributed optimization problem over an undirected multi-agent network, where each agent is assigned a private cost function which is strongly convex with a Lipschitz gradient. First, an event-triggered communication strategy is adopted to reduce network communication, and an event-based auxiliary system is constructed to estimate the optimal solution. The estimation is used to generate a reference signal and its high-order derivatives, which is an approximation of the optimal solution. Then, a backstepping control algorithm is developed to drive all agents' output to the reference signal. In terms with the Lyapunov stability theory and the algebraic graph theory, it can be proved that the distributed optimization problem is solved by the developed control algorithm. Different from the existing algorithms, eigenvalues of the Laplacian matrix are not used in our proposed control design. Finally, a simulation example is presented to validate the theoretical result.

关键词： Laplace equations Eigenvalues and eigenfunctions Cost function Backstepping Communication networks Behavioral sciences Multi-agent systems Event-triggered control distributed optimization multiagent systems

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distributed optimization for intelligent IoT under unstable communication conditions

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COMPUTER COMMUNICATIONS 2023年第1期199卷 42-49页

作者： Yuan, Yuan Yu, Jiguo Zhang, Liangxu Cai, Zhipeng Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan 250353 Shandong Peoples R China Qilu Univ Technol Big Data Inst Shandong Acad Sci Jinan 250353 Shandong Peoples R China Qilu Univ Technol Shandong Acad Sci Shandong Fundamental Res Ctr Comp Sci Jinan 250353 Shandong Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan 266237 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

The design of distributed optimization algorithms for intelligent Internet of Thing (IIoT) environments has attracted extensive attention. However, the dynamic network environment, the delay of information transmission between devices and the risk of privacy disclosure pose great challenges to algorithm designs. To address this problem, we propose a distributed gradient descent algorithm, which can converge under very mild assumptions that there will be an unbounded (stochastic) delay from sender to receiver. To the best of our knowledge, our proposed algorithm is the first work to take unbounded stochastic communication delays into consideration, in addition to time-varying networks and privacy disclosure. Rigorous analysis shows that our algorithm can converge under such mild assumptions. Extensive experiments exhibit that the proposed algorithm performs well in complex IIoT environments.

关键词： distributed optimization Intelligent IoT Unstable communications

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