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distributed constrained optimization problem of heterogeneous linear multi-agent systems with communication delays

SYSTEMS & CONTROL LETTERS 2021年 155卷 105002-105002页

作者： Liu, Pin Xiao, Feng Wei, Bo Wang, Aiping North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Sch Math & Phys Beijing 102206 Peoples R China

This paper addresses the constrained distributed optimization problem of heterogeneous linear multiagent systems, where the agents with linear dynamics are subject to local set constraints, global nonlinear inequality constraints and heterogeneous communication delays. Agents collaborate to minimize a global objective function by communicating with their neighbors in a graph. Each agent's decision variable is constrained in a local set. The decision variables of all agents are coupled by global inequality constraints which are modeled by nonlinear functions. To handle the heterogeneous constant communication delays, the scattering transformation between neighbors is employed. We design a new distributed control law to investigate the passivity of systems of individual agents in the presence of constraints and communication delays. Integrating the proposed control law with the scattering transformation, we prove that systems converge to the optimal solution which minimizes the global objective functions. Simulations of heterogeneous linear multi-agent systems are presented to illustrate the effectiveness of the distributed control law. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Communication delays distributed optimization problem Heterogeneous multi-agent systems Proportional-integral algorithms

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distributed Optimal Consensus Control for Heterogeneous Multi-agent System with Disturbance 18th

Distributed Optimal Consensus Control for Heterogeneous Mult...

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18th International Conference on Neural Networks (ISNN)

作者： Chai, Yiyuan Qin, Sitian Feng, Jiqiang Xu, Chen Shenzhen Univ Sch Elect & Informat Engn Shenzhen 518060 Peoples R China Harbin Inst Technol Dept Math Weihai 264209 Peoples R China Shenzhen Univ Coll Math Sci Shenzhen 518060 Peoples R China

ISBN: (纸本)9789819743988;9789819743995

This paper develops a distributed control protocol for the distributed optimal consensus problem of multi-agent systems. The dynamics of multi-agent systems are heterogeneous linear. Considering that multi-agent systems often face various external disturbances in practical applications, which may seriously affect the performance and stability of the system. The control protocol proposed in this paper pays special attention to dealing with external disturbances in linear dynamic systems. Theoretical results show that the distributed control protocol is robust under the external disruption and drives the states of each agent to the auxiliary variable in a finite time. Further, with the aid of Lyapunov method, the states of close-loop system globally converge to the optimal solution of the non-smooth optimization problem. Finally, the simulation results demonstrate the effectiveness of proposed control protocol.

关键词： distributed optimization problem Heterogeneous multi-agent systems External disturbances Finite-time convergence

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A projection-based continuous-time algorithm for distributed optimization over multi-agent systems

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COMPLEX & INTELLIGENT SYSTEMS 2022年第2期8卷 719-729页

作者： Wen, Xingnan Qin, Sitian Harbin Inst Technol Dept Math Weihai 264209 Peoples R China

Multi-agent systems are widely studied due to its ability of solving complex tasks in many fields, especially in deep reinforcement learning. Recently, distributed optimization problem over multi-agent systems has drawn much attention because of its extensive applications. This paper presents a projection-based continuous-time algorithm for solving convex distributed optimization problem with equality and inequality constraints over multi-agent systems. The distinguishing feature of such problem lies in the fact that each agent with private local cost function and constraints can only communicate with its neighbors. All agents aim to cooperatively optimize a sum of local cost functions. By the aid of penalty method, the states of the proposed algorithm will enter equality constraint set in fixed time and ultimately converge to an optimal solution to the objective problem. In contrast to some existed approaches, the continuous-time algorithm has fewer state variables and the testification of the consensus is also involved in the proof of convergence. Ultimately, two simulations are given to show the viability of the algorithm.

关键词： Deep reinforcement learning Multi-agent systems distributed optimization problem Penalty method Convergence

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A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems

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NEURAL NETWORKS 2021年 143卷 52-65页

作者： Wen, Xingnan Luan, Linhua Qin, Sitian Harbin Inst Technol Dept Math Weihai Peoples R China

distributed optimization problem (DOP) over multi-agent systems, which can be described by minimizing the sum of agents' local objective functions, has recently attracted widespread attention owing to its applications in diverse domains. In this paper, inspired by penalty method and subgradient descent method, a continuous-time neurodynamic approach is proposed for solving a DOP with inequality and set constraints. The state of continuous-time neurodynamic approach exists globally and converges to an optimal solution of the considered DOP. Comparisons reveal that the proposed neurodynamic approach can not only resolve more general convex DOPs, but also has lower dimension of solution space. Additionally, the discretization of the neurodynamic approach is also introduced for the convenience of implementation in practice. The iteration sequence of discrete-time method is also convergent to an optimal solution of DOP from any initial point. The effectiveness of the neurodynamic approach is verified by simulation examples and an application in L-1-norm minimization problem in the end. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： distributed optimization problem Neurodynamic approach Penalty method Multi-agent systems Convergence

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Online Algorithms for Information Aggregation From distributed and Correlated Sources

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IEEE-ACM TRANSACTIONS ON NETWORKING 2016年第6期24卷 3714-3725页

作者： Chau, Chi-Kin Khonji, Majid Aftab, Muhammad Masdar Inst Sci & Technol Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates

There is a fundamental tradeoff between the communication cost and the latency in information aggregation. Aggregating multiple communication messages over time can alleviate overhead and improve energy efficiency on one hand, but inevitably incurs information delay on the other hand. In the presence of uncertain future inputs, this tradeoff should be balanced in an online manner, which is studied by the classical dynamic TCP ACK problem for a single information source. In this paper, we extend dynamic TCP ACK problem to a general setting of collecting aggregate information from distributed and correlated information sources. In this model, distributed sources observe correlated events, whereas only a small number of reports are required from the sources. The sources make online decisions about their reporting operations in a distributed manner without prior knowledge of the local observations at others. Our problem captures a wide range of applications, such as in-situ sensing, anycast acknowledgement, and distributed caching. We present simple threshold-based competitive distributed online algorithms under different settings of intercommunication. Our algorithms match the theoretical lower bounds in order of magnitude. We observe that our algorithms can produce satisfactory performance in simulations and practical test bed.

关键词： Online algorithms competitive analysis distributed optimization problem latency communication cost

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