检索结果-内蒙古大学图书馆

distributed optimization without boundedness of gradients for second-order multi-agent systems over unbalanced network

引用

INFORMATION SCIENCES 2021年 565卷 177-195页

作者： Mo, Lipo Hu, Haokun Yu, Yongguang Ren, Guojian Beijing Technol & Business Univ Sch Math & Stat Beijing 100048 Peoples R China Guizhou Inst Technol Key Lab Power Big Data Guizhou Prov Guiyang 550003 Peoples R China Beijing Jiaotong Univ Dept Math Beijing 10044 Peoples R China

This paper is mainly devoted to the distributed second-order multi-agent optimization problem with unbalanced and directed networks, where the gradient of the private objective functions might be unbounded. To deal with this problem, a new distributed algorithm is proposed based on the local neighbor information and the private objective functions. By a coordination transformation, the closed-loop system is divided into two simple first order subsystems. Under the assumption of the strong connectivity of networks, it is proved that all agents can collaboratively converge to some optimal solution of the team objective function. At last, we give two numerical examples to show the effectiveness of our proposed algorithm. (c) 2021 Elsevier Inc. All rights reserved.

关键词： distributed optimization Second-order multi-agent systems Unbalanced network

来源：评论

学校读者我要写书评

暂无评论

distributed optimization of Uncertain Multiagent Systems With Disturbances and Actuator Faults via Exosystem Observer-Based Output Regulation

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第2期70卷 897-909页

作者： Kang, Jian Guo, Ge Yang, Guanghong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This paper investigates a distributed optimization problem for heterogeneous linear multi-agent systems with uncertain parameters and external disturbances. For each agent, the optimal solution of the global objective functions can be regarded as an unknown constant reference signal. From this angle, the problem is transformed into an output regulation problem for an exosystem whose states and outputs are to be estimated. To ensure the trackability of the estimated signal, a fundamental design condition for the exosystem observer to achieve robust output regulation is given in the context of internal model control. Exact global optimization is then realized by embedding an appropriate distributed optimization algorithm (i.e., the exosystem observer) in each agent to estimate the global optimal solution. The result for exosystem observer design is extended to yield an adaptive fault-tolerant control algorithm to deal with a class of practical actuator fault uncertainties. The exosystem observer-based output regulation method provides a general distributed robust optimization framework for uncertain systems with disturbances and actuator faults, which can directly apply to various consensus problems. Simulations are provided to illustrate the effectiveness of the method.

关键词： distributed optimization linear systems robust output regulation fault-tolerant control multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

distributed optimization Method for Spacecraft Attitude Control and Vibration Suppression

引用

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2023年第4期46卷 752-760页

作者： Hu, Yabo Bernelli-Zazzera, Franco Geng, Yunhai Harbin Inst Technol Res Ctr Satellite Technol Harbin 150001 Peoples R China Polytech Univ Milan Dept Aerosp Sci & Technol I-20156 Milan Italy

来源：评论

学校读者我要写书评

暂无评论

distributed optimization, Averaging via ADMM, and Network Topology

引用

PROCEEDINGS OF THE IEEE 2020年第11期108卷 1939-1952页

作者： Franca, Guilherme Bento, Jose Johns Hopkins Univ Math Inst Data Sci Baltimore MD 21218 USA Boston Coll Dept Comp Sci Boston MA 02467 USA

There has been an increasing necessity for scalable optimization methods, especially due to the explosion in the size of data sets and model complexity in modern machine learning applications. Scalable solvers often distribute the computation over a network of processing units. For simple algorithms, such as gradient descent, the dependence of the convergence time with the topology of this network is well known. However, for more involved algorithms, such as the alternating direction method of multipliers (ADMM), much less is known. At the heart of many distributed optimization algorithms, there exists a gossip subroutine which averages local information over the network, whose efficiency is crucial for the overall performance of the method. In this article, we review recent research in this area, and with the goal of isolating such a communication exchange behavior, we compare different algorithms when applied to a canonical distributed averaging consensus problem. We also show interesting connections between ADMM and the lifted Markov chains besides providing an explicit characterization of its convergence and optimal parameter tuning in terms of spectral properties of the network. Finally, we empirically study the connection between network topology and convergence rates for different algorithms on a real-world problem of sensor localization.

关键词： Convergence optimization methods Convex functions Eigenvalues and eigenfunctions Machine learning Network topology Markov processes Alternating direction method of multipliers (ADMM) average consensus distributed optimization lifted Markov chains

来源：评论

学校读者我要写书评

暂无评论

distributed optimization Over Time-Varying Graphs With Imperfect Sharing of Information

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第7期68卷 4420-4427页

作者： Reisizadeh, Hadi Touri, Behrouz Mohajer, Soheil Univ Minnesota Minneapolis MN 55455 USA Univ Calif San Diego La Jolla 92093 CA USA

We study strongly convex distributed optimization problems where a set of agents are interested in solving a separable optimization problem collaboratively. In this article, we propose and study a two-time-scale decentralized gradient descent algorithm for a broad class of lossy sharing of information over time-varying graphs. One time-scale fades out the (lossy) incoming information from neighboring agents, and one time-scale regulates the local loss functions' gradients. We show that assuming a proper choice of step-size sequences, certain connectivity conditions, and bounded gradients along the trajectory of the dynamics, the agents' estimates converge to the optimal solution with the rate of O(T-1/2). We also provide novel tools to study distributed optimization with diminishing averaging weights over time-varying graphs.

关键词： Convex optimization distributed multiagent system distributed optimization gradient descent algorithms time-varying graphs

来源：评论

学校读者我要写书评

暂无评论

distributed optimization Design for Computation of Algebraic Riccati Inequalities

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1924-1935页

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

This article proposes a distributed optimization design to compute continuous-time algebraic Riccati inequalities (ARIs), where the information of matrices is distributed among agents. We propose a design procedure to tackle the nonlinearity, the inequality, and the coupled information structure of ARI;then, we design a distributed algorithm based on an optimization approach and analyze its convergence properties. The proposed algorithm is able to verify whether ARI is feasible in a distributed way and converges to a solution if ARI is feasible for any initial condition.

关键词： Linear matrix inequalities distributed algorithms optimization Heuristic algorithms Dynamical systems Convergence Multi-agent systems distributed algorithm distributed optimization matrix inequality

来源：评论

学校读者我要写书评

暂无评论

distributed optimization With Improved Dynamic Performance for Multiagent Systems

引用

IEEE ACCESS 2022年 10卷 78002-78010页

作者： Liaqat, Ifrah Tahir, Muhammad Fayyaz, Ubaid Ullah Univ Engn & Technol Dept Elect Engn Lahore 54890 Pakistan

Dynamic coverage is one of the fundamental problems in multi-agent systems (MASs), and is related to optimal placement of nodes to observe a physical space. In a typical coverage problem, a set of targets are required to be monitored. The problem becomes more challenging when the targets are allowed to move as well. Efficient coverage control by mobile agents in a specific area poses many challenges, such as optimal coverage of all targets, dynamic redeployment of agents as targets change their location, trajectory control of each agent during redeployment and determining the number of mobile agents to cover specific targets. In particular, for dynamic coverage problems, agents are deployed to provide coverage to the mobile targets and the agents dynamically redeploy themselves in such a way that they provide maximum coverage to targets not only when they are stationary but also when they are in motion. In many scenarios, such as disaster recovery or public event coverage, dynamic behavior of agents to reach to the next optimal position, plays an important role in determining the performance of the system. In this paper, we propose an augmented Lagrangian based algorithm, which provides a mechanism to control the trajectory of agents to reach the optimal position. By adjusting the gain parameters of the proposed algorithm, we achieve negligible overshoot in response to fast dynamics that is not possible by using conventional Lagrangian. Also, the proposed algorithm is close to the optimal trajectory. Thus, by using the proposed algorithm, we can improve the dynamic performance without compromising the optimal deployment of the agents. The numerical evaluation results show significant improvement in dynamic performance for an example scenario of an MAS.

关键词： Trajectory optimization Target tracking Heuristic algorithms Multi-agent systems Transient analysis Sensors distributed optimization multi agent dynamical system optimal coverage problem optimized transient control tracking trajectory

来源：评论

学校读者我要写书评

暂无评论

distributed optimization of Multiagent Systems With Preserved Network Connectivity

引用

IEEE TRANSACTIONS ON CYBERNETICS 2019年第11期49卷 3980-3990页

作者： Ning, Boda Han, Qing-Long Zuo, Zongyu Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Beihang Univ Seventh Res Div Beijing 100191 Peoples R China

This paper deals with the problem of distributed optimization of a multiagent system with network connectivity preservation. In order to realize cooperative interactions, a connected network is the prerequisite for high-quality information exchange among agents. However, sensing or communication capability is range-limited, so it is impractical to simply make an assumption that network connectivity is preserved by default. To address this concern, a class of generalized potentials including discontinuities caused by unexpected obstacles or noises are designed. For a class of quadratic cost functions, based on the potentials, a new distributed protocol is proposed to formally guarantee the network connectivity over time and to realize the state agreement in finite time while the sum of local functions known to individual agents is optimized. Since the right-hand side of the proposed protocol is discontinuous, some nonsmooth analysis tools are applied to analyze system performance. In some practical scenarios, where initial states are unavailable, a distributed protocol is further developed to realize the consensus in a prescribed finite time while solving the distributed optimization problem and maintaining network connectivity. Illustrative examples are provided to demonstrate the effectiveness of the proposed protocols.

关键词： distributed optimization finite-time consensus multiagent systems network connectivity

来源：评论

学校读者我要写书评

暂无评论

distributed optimization algorithm for multi-agent optimization problems using consensus control

引用

JOURNAL OF ADVANCED MECHANICAL DESIGN SYSTEMS AND MANUFACTURING 2024年第5期18卷

作者： Nishi, Tatsushi Debuchi, Naoto Liu, Ziang Okayama Univ Sch Nat Sci & Technol 3-1-1 Tsushima-naka Kita-ku Okayama Okayama 7008530 Japan

The distributed optimization algorithms using consensus control are proposed for solving multi-agent optimization problems. The multi-agent optimization problem has discrete and continuous decision variables to minimize the sum of local cost functions with local and global constraints. The problem is formulated as a mixed integer programming problem. In this study, we propose two distributed optimization algorithms to solve the problem. A feasible solution is obtained by solving the continuous optimization problem using an existing distributed optimization method with consensus control and solving the discrete optimization problem by fixing 0-1 variables. In the proposed method 1, all possible combinations of a binary variables are searched. Since all combinations are searched, the exact optimal solution can be obtained. However, it is difficult to apply to largescale problems. In the proposed method 2, we derive the solution of binary variables by using Lagrangian decomposition and coordination approach. The proposed method 2 can provide approximate solutions for more complex problems within a practical computation time. These methods are successfully implemented to obtain near-optimal solutions in a distributed environment for supply chain planning problems for multiple companies and multi-agent unit commitment problem. The number of information exchanges in the two proposed methods is evaluated. The information exchange for these methods can significantly reduce the data exchange compared with the conventional centralized optimization method. Computational experiments for the multi-agent unit commitment problems and supply chain planning problems for multiple companies demonstrate the effectiveness of the proposed methods.

关键词： distributed optimization Unit commitment Supply chain planning Subgradient method Lagrangian decomposition and coordination approach

来源：评论

学校读者我要写书评

暂无评论

distributed optimization for Linear Multiagent Systems: Edge- and Node-Based Adaptive Designs

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2017年第7期62卷 3602-3609页

作者： Zhao, Yu Liu, Yongfang Wen, Guanghui Chen, Guanrong Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Southeast Univ Dept Math Nanjing 210096 Jiangsu Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China

This paper studies the distributed optimization problem for continuous-time multiagent systems with general linear dynamics. The objective is to cooperatively optimize a team performance function formed by a sum of convex local objective functions. Each agent utilizes only local interaction and the gradient of its own local objective function. To achieve the cooperative goal, a couple of fully distributed optimal algorithms are designed. First, an edge-based adaptive algorithm is developed for linear multiagent systems with a class of convex local objective functions. Then, a node-based adaptive algorithm is constructed to solve the distributed optimization problem for a class of agents satisfying the bounded-input bounded-state stable property. Sufficient conditions are given to ensure that all agents reach a consensus while minimizing the team performance function. Finally, numerical examples are provided to illustrate the theoretical results.

关键词： Adaptive approach convex optimization distributed optimization linear system multiagent system

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：