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distributed optimization of networked marine surface vehicles: A fixed-time estimator-based approach

OCEAN ENGINEERING 2023年第1期284卷

作者： Chen, Qian Ge, Ming-Feng Liang, Chang-Duo Gu, Zhi-Wei Liu, Jie China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

This paper focuses on the fixed-time consensus problem of the networked marine surface vehicles (NMSVs) while minimizing the sum of the cost functions. To tackle this complex problem, we propose a fixed-time optimization hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time optimization estimation problem and a local fixed-time tracking problem respectively. Specifically, the proposed approach leverages a distributed fixed-time optimization estimator to enable the NMSVs to estimate the state and velocity of a virtual leader while minimizing the sum of cost functions. Additionally, the above estimated signal will be tracked by the NMSVs under a fixed-time local tracking controller. We demonstrate the effectiveness of our proposed approach through simulation experiments on a network of four marine surface vehicles, and present the sufficient conditions for stability through Lyapunov stability analysis.

关键词： Networked marine surface vehicles (NMSVs) distributed optimization Fixed-time estimator Hierarchical controller synthesis

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distributed optimization via primal and dual decompositions for delay-constrained FANETs

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AD HOC NETWORKS 2020年 109卷 102288-102288页

作者： Wen, Shaojie Deng, Lianbing Liu, Yuhang Zhuhai Da Hengqin Sci & Technol Dev Co Ltd Zhuhai Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

This paper aims to optimize the different network parameters in a distributed manner for delay-constrained flying ad hoc networks (FANETs) without the global network topology information. To this end, each Unmaimed Aerial Vehicle (UAV) calculates the average interference level during a certain time period to indicate the channel states. Next, we formulate the distributed optimization problem as a utility maximization problem, which jointly optimizes power control, rate allocation and delay-constrained routing. To obtain a distributed solution, a dual method is proposed to eliminate the link capacity constraint, and a primal decomposition method is employed to decouple the end-to-end delay constraint. Built on these two methods above, a distributed optimization algorithm is proposed in this work by considering the estimated one-hop delay for each transmission, which only uses the local channel information to optimize the sub-problems and limit the end-to-end delay. Finally, we deduce the relationship between the primal and dual solutions to underpin the advantage of the proposed algorithm. Experiments on simulate (and real) data demonstrate that the proposed algorithm effectively can improve network performances in terms of energy efficiency, packet timeout ratio and network throughput.

关键词： Flying ad hoc networks distributed optimization Primal and dual decomposition Delay constraint

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distributed optimization for Uncertain High-Order Nonlinear Multiagent Systems via Dynamic Gain Approach

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第7期53卷 4351-4357页

作者： Ma, Qian Meng, Qingtan Xu, Shengyuan Nanjing Univ Sci & Technol Sch Automation Nanjing 210094 Peoples R China

In this article, we investigate the distributed output optimization for general uncertain high-order nonlinear multiagent systems (MASs), where nonlinear functions are constrained by a linear growth condition. The dynamic gain approach is utilized to cope with the influence of the unknown optimal solution. First, the distributed optimal coordinators (DOCs) with an adjustable parameter are constructed to steer the generated signals converging to the optimal solution. By developing the iterative design strategy, the dynamic reference-tracking controllers are then designed so that the output of each agent follows the generated value of coordinators, respectively. It is proved that all states are globally bounded, as well as the tracking error between the outputs and optimal solution can be bounded in a finite time by an arbitrarily small constant. Simulation studies demonstrate the validness of the main idea.

关键词： optimization Multi-agent systems Linear systems Linear programming Delays Cybernetics Uncertainty distributed optimization dynamic gain approach multiagent systems (MASs) uncertain nonlinear systems

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distributed optimization With Global Constraints Using Noisy Measurements

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第2期69卷 1089-1096页

作者： Mai, Van Sy La, Richard J. Zhang, Tao Battou, Abdella Natl Inst Stand & Technol NIST Gaithersburg MD 20899 USA Univ Maryland College Pk MD 20742 USA

We propose a new distributed optimization algorithm for solving a class of constrained optimization problems in which the objective function is separable (i.e., the sum of local objective functions of agents), the optimization variables of distributed agents, which are subject to nontrivial local constraints, are coupled by global constraints, and only noisy observations are available to estimate (the gradients of) local objective functions. In many practical scenarios, agents may not be willing to share their optimization variables with others. For this reason, we propose a distributed algorithm that does not require the agents to share their optimization variables with each other;instead, each agent maintains a local estimate of the global constraint functions and shares the estimate only with its neighbors. These local estimates of constraint functions are updated using a consensus-type algorithm, whereas the local optimization variables of each agent are updated using a first-order method based on noisy estimates of gradient. We prove that, when the agents adopt the proposed algorithm, their optimization variables converge with probability 1 to an optimal point of an approximated problem based on the penalty method.

关键词： distributed optimization penalty method stochastic optimization

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distributed optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第8期47卷 2058-2066页

作者： Wang, Xinghu Hong, Yiguang Yi, Peng Ji, Haibo Kang, Yu Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Chinese Acad Sci Inst Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada Univ Sci & Technol China State Key Lab Fire Sci Hefei 230027 Peoples R China

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

关键词： Adaptive internal model distributed optimization multiagent systems unknown-frequency disturbance

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distributed optimization-Based Dynamic Tariff for Congestion Management in Distribution Networks

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IEEE TRANSACTIONS ON SMART GRID 2019年第1期10卷 184-192页

作者： Huang, Shaojun Wu, Qiuwei Zhao, Haoran Li, Canbing Tech Univ Denmark Dept Elect Engn Ctr Elect Power & Energy DK-2800 Lyngby Denmark Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

This paper proposes a distributed optimization-based dynamic tariff (DDT) method for congestion management in distribution networks with high penetration of electric vehicles and heat pumps. The DDT method employs a decomposition-based optimization method to have aggregators explicitly participate in congestion management, which gives more certainty and transparency compared to the normal DT method. With the DDT method, aggregators reveal their final aggregated plan and respect the plan during operation. By establishing an equivalent overall optimization, it is proven that the DDT method is able to minimize the overall energy consumption cost and line loss cost, which is different from the previous decomposition-based methods such as multiagent system methods. In addition, a reconditioning method and an integral controller are introduced to improve convergence of the distributed optimization where challenges arise due to multiple congestion points, multiple types of flexible demands, and network constraints. The case studies demonstrate the efficacy of the DDT method for congestion management in distribution networks.

关键词： Congestion management distributed optimization distribution system operator (DSO) dynamic tariff electric vehicle (EV) heat pump (HP)

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distributed optimization for Autonomous Restoration in DER-Rich Distribution System

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IEEE TRANSACTIONS ON POWER DELIVERY 2023年第5期38卷 3205-3217页

作者： Konar, Srayashi Srivastava, Anurag K. Dubey, Anamika Washington State Univ WSU Sch Elect Engn & Comp Sci Pullman WA 99163 USA West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

Autonomous service restoration (ASR) of active distribution network (ADN) reduces service restoration time with the help of measurement devices, smart switches and distributed energy resources (DERs) considering the system's operational and radiality constraints. Restoration scheme can be deployed in both centralized and distributed manner. However, with increasing data points, the requirement for measurement availability at the control center for the decision support makes centralized optimization challenging. The decomposition and coordination scheme of distributed algorithms enhances its ability to solve large scale optimization problems. Moreover, distributed optimization enables robustness, scalability and resiliency compared to centralized optimization. In this work, a) the distributed ASR problem is solved using a novel penalty-driven distributed alternating direction method of multipliers algorithm (PD-ADMM), b) a switch level decomposition and coordination technique is proposed, c) load restoration, DER utilization are considered directly and radiality of ADN is enforced using commodity flow model. The applicability of the algorithm is validated using modified IEEE 33-bus, IEEE 123-bus and 1069-bus test systems for contingency cases, communication failure, scalability, accuracy and sensitivity to number of partitions, starting points, change in DER/solar generation.

关键词： Active distribution network autonomous restoration distributed optimization mixed integer problem penalty function

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distributed optimization for MPC of Linear Networks With Uncertain Dynamics

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2012年第3期57卷 804-809页

作者： Camponogara, Eduardo de Lima, Marcelo Lopes Univ Fed Santa Catarina Dept Automat & Syst Engn BR-88040900 Florianopolis SC Brazil

A linear dynamic network consists of a directed graph whose nodes are subsystems and whose arcs define dynamic couplings. Subsystem states evolve depending on the local and upstream control signals according to uncertain dynamics. Dynamic networks can serve as models for geographically distributed systems such as traffic networks and petrochemical plants. This technical note develops a distributed algorithm to operate a linear dynamic network with a network of agents that implement a distributed model predictive control strategy. Based on subgradient optimization to handle nondifferentiability, the distributed algorithm is shown to converge to an optimal solution.

关键词： distributed optimization model predictive control subgradient optimization systems of systems

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distributed optimization for Resilient Transmission of Confidential Information in Interference Channels

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2017年第1期66卷 494-501页

作者： Zhang, Zhiyu Teh, Kah Chan Li, Kwok Hung Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this paper, resilient transmission in a multi-cell multiple-input multiple-output (MIMO) interference wiretap channel model is studied. Each base station (BS) transmits confidential messages to its intended legitimate user with multiple antennas in the presence of eavesdroppers that can overhear the transmission. We study the problem of finding the optimal transmit covariance matrices at the BSs to maximize the secrecy sum rate, which is typically nonconvex and intractable to obtain a globally optimal solution. A distributed iterative optimization algorithm based on a novel decomposition framework across all users is proposed. The decomposition framework preserves the convexity of the objective function and linearizes the nonconvex part. At each iteration, the BSs simultaneously solve a sequence of problems that are decoupled convex approximations of the original secrecy sum-rate function. Moreover, the complexity of the algorithm is analyzed. Numerical results are presented to validate the effectiveness of the proposed distributed algorithm.

关键词： distributed optimization multiple-antenna systems physical-layer security successive convex approximation (SCA)

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distributed optimization for consensus performance of delayed fractional-order double-integrator multi-agent systems

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NEUROCOMPUTING 2023年第1期522卷 105-115页

作者： Liu, Jun Zhou, Nan Qin, Kaiyu Chen, Badong Wu, Yonghong Choi, Kup-Sze Chengdu Univ Informat Technol Sch Automat Chengdu Peoples R China Chengdu Univ Sch Elect Informat & Elect Engn Chengdu Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Wuhan Univ Technol Sch Sci Wuhan Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Hong Kong Peoples R China

This paper addresses distributed optimization problems concerning consensus in delayed fractional -order double-integrator multi-agent systems (FDMSs). To start with, an optimized distributed protocol with state-fractional-order-derivative feedback (SF) is presented for delayed FDMSs. Then, the consensus problems are studied for the two kinds of delayed FDMSs with SF in the presence of symmetric time -delays over undirected network topology and asymmetric time-delays over directed network topology. Next, by the means of graph theory, matrix theory and frequency-domain analysis method, the sufficient conditions to guarantee consensus of delayed FDMSs with SF are derived. Compared to the traditional distributed protocol without SF, the proposed distributed optimization protocol with SF are taken into account to enable better consensus performance in delayed FDMSs with SF. Finally, numerical experi-ments are carried out to verify the feasibility of our theoretical results.(c) 2022 Elsevier B.V. All rights reserved.

关键词： distributed optimization Consensus Fractional-order Double-integrator Multi-agent systems Network topology State-fractional-order-derivative feedback Time-delays

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