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Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

作者： Yi Dong Zhongguo Li Xingyu Zhao Zhengtao Ding Xiaowei Huang University of Liverpool Liverpool United Kingdom University College London London United Kingdom University of Manchester Manchester United Kingdom

ISBN: (纸本)9781450394321

Multi-robot cooperative control has gained extensive research interest due to its wide applications in civil, security, and military domains. This paper proposes a cooperative control algorithm for multi-robot systems with general linear dynamics. The algorithm is based on distributed cooperative optimisation and output regulation, and it achieves global optimum by utilising only information shared among neighbouring robots. Technically, a high-level distributed optimisation algorithm for multi-robot systems is presented, which will serve as an optimal reference generator for each individual agent. Then, based on the distributed optimisation algorithm, an output regulation method is utilised to solve the optimal coordination problem for general linear dynamic systems. The convergence of the proposed algorithm is theoretically proved. Both numerical simulations and real-time physical robot experiments are conducted to validate the effectiveness of the proposed cooperative control algorithms.

关键词： cooperative control output regulation optimal coordination multi-robot systems distributed optimisation

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distributed model-free optimisation in community-based energy market

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年

作者： Asgari, Houman Babazadeh, Maryam Tech Univ Munich Sch Computat Informat & Technol Munich Germany Sharif Univ Technol Dept Elect Engn Tehran Iran

A distributed data-driven algorithm is proposed for market clearing within a community-based energy market. The objective is to maximise social welfare in energy trading. Each participant in the market, known as a prosumer, employs an extremum-seeking control (ESC) algorithm within a consensus-based architecture based on simple third-order dynamics. The proposed method circumvents the need for a central coordinator in the market clearing process. Furthermore, prosumers do not require analytical and exact formulations of cost or utility functions. They are required to transmit only a single local decision variable to the neighbours through an undirected connected graph. Under the assumption that prosumers have strongly convex local objective functions, the proposed algorithm demonstrates semi-global practical asymptotic (SPA) convergence to the optimal solution. This convergence is established using averaging theory. Simulation results validate the effectiveness, scalability, and robustness of the proposed distributed strategy.

关键词： distributed optimisation extremum-seeking averaging theory community-based energy market

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Zero gradient sum algorithm of arbitrary initial value with constraints and communication delay based on directed graph

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年第2期56卷 267-292页

作者： Lin, Siman Zhang, Xiaotang Ren, Junwu Tan, Manchun Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

The distributed convex optimisation issue with inequality constraints and box constraints on the directed communication topology is examined in this paper. In the case of the communication delay between agents, we propose a piecewise zero-gradient-sum triggered control algorithm that allows arbitrary initial values. Using the log-barrier penalty method, we only need to study an unconstrained approximation problem of the original problem. Firstly, each agent's state converges to the optimal value of the related local objective function in a fixed time. Simultaneously, the upper bound of fixed time in this paper is smaller than some existing results. Then, in the case of communication delay, time-triggered and event-triggered methods with non-uniform sampling interval are proposed in this paper, which make the communication cost lower and the application broader. Using the Lyapunov function method, the sufficient conditions for each agent's state to converge to the optimal point are given, and the Zeno behaviour is excluded. Finally, to confirm the algorithm's effectiveness, we provide two numerical examples and one machine learning example as demonstrations.

关键词： distributed optimisation zero-gradient-sum algorithm constraints trigger control mechanism communication delay

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Multilayered composite structure design optimisation using distributed/parallel multi-objective evolutionary algorithms

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COMPOSITE STRUCTURES 2012年第3期94卷 1087-1096页

作者： Lee, D. S. Morillo, C. Bugeda, G. Oller, S. Onate, E. Int Ctr Numer Methods Engn CIMNE Barcelona 08034 Spain Univ Politecn Cataluna Barcelona 08034 Spain

This paper presents a research work on stacking sequence design optimisation for multilayered composite plate using a parallel/distributed evolutionary algorithm. The stacking sequence of fibres has a dramatic influence on the strength of multilayered composite plates. Multiple layers of fibre-reinforced material systems offer versatility in engineering material design due to the fact that the stacking sequence of each orthotropic layer can offer full advantage of superior mechanical properties. Numerical results show that the optimal composite structures have lower weight, higher stiffness and also affordable cost when compared to the extreme and intermediate composite structures. In addition, the benefits of using a parallel optimisation system are also presented. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multilayered composite structure Fibre orientation Stacking sequence Multi-objective/multidisciplinary design distributed optimisation Evolutionary algorithms

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distributed modifier-adaptation schemes for the real-time optimisation of uncertain interconnected systems

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INTERNATIONAL JOURNAL OF CONTROL 2019年第5期92卷 1123-1136页

作者： Schneider, Rene Milosavljevic, Predrag Bonvin, Dominique Ecole Polytech Fed Lausanne Lab Automat Lausanne Switzerland

This paper proposes a set of distributed real-time optimisation schemes for the steady-state optimisation of parametrically and structurally uncertain systems that are composed of multiple interconnected subsystems. These schemes are derived in the framework of modifier adaptation and use uncertain system models of varying complexity. Consequently, each distributed modifier-adaptation scheme imposes different requirements on the available plant measurements, the communication network, and the control architecture. We prove that every scheme can converge to the plant optimum despite model uncertainty and compare the main features of each approach. Finally, we illustrate our findings via a numerical example.

关键词： distributed optimisation interconnected systems real-time optimisation modifier adaptation model uncertainty

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distributed primal-dual optimisation method with uncoordinated time-varying step-sizes

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第6期49卷 1256-1272页

作者： Liu, Ping Li, Huaqing Dai, Xiangguang Han, Qi Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Chongqing Univ Sci & Technol Coll Elect & Informat Engn Chongqing Peoples R China

This paper is concerned with the distributed optimisation problem over a multi-agent network, where the objective function is described by a sum of all the local objectives of agents. The target of agents is to collectively reach an optimal solution while minimising the global objective function. Under the assumption that the information exchange among agents is depicted by a sequence of time-varying undirected graphs, a distributed optimisation algorithm with uncoordinated time-varying step-sizes is presented, which signifies that the step-sizes of agents are not always uniform per iteration. In light of some reasonable assumptions, this paper fully conducts an explicit analysis for the convergence rate of the optimisation method. A striking feature is that the algorithm has a geometric convergence rate even if the step-sizes are time-varying and uncoordinated. Simulation results on two numerical experiments in power systems show effectiveness and performance of the proposed algorithm.

关键词： distributed optimisation uncoordinated time-varying step-sizes linear convergence small gain theorem

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An optimisation-oriented model of distributed supply-chain

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MATHEMATICS AND COMPUTERS IN SIMULATION 2008年第4期79卷 937-946页

作者： Ghirardi, Marco Menga, Giuseppe Sacco, Nicola Politecn Torino Dept Control & Comp Sci I-10129 Turin Italy Univ Genoa Dept Fluid Machines Energy Syst & Transportat I-16145 Genoa Italy

In this paper, a Suitable model of distributed supply-chains (DSCs) is presented with the aim of providing a tool for DSC decentralised optimisation. To cope with this challenge, in the first part of the paper, a general model for distributed supply-chain including suppliers, processing units. assemblers, and transportation systems is presented with the aim of keeping the framework as general as possible. In the second part of the paper, an optimisation algorithm is also discussed. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Supply-chain distributed optimisation Lagrangian relaxation

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distributed finite-time optimisation for multi-agent systems via event-triggered aperiodically intermittent communication

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2022年第8期53卷 1674-1689页

作者： Yang, Feiyang Yu, Zhiyong Jiang, Haijun Xinjiang Univ Coll Math & Syst Sci Urumqi 830046 Peoples R China

In this paper, the finite-time distributed optimisation problem for multi-agent systems (MASs) is investigated by proposing a kind of new event-based aperiodically intermittent communication strategy. Firstly, the distributed optimisation problem with the sum of local objective functions is considered. A novel finite-time event-triggered intermittent control protocol is proposed over undirected networks, and some sufficient conditions are obtained to ensure the finite-time consensus of MASs and the asymptotical solvability of the optimisation problem. Secondly, a more general distributed optimisation problem, in which the optimisation objective is the convex combination of local objective functions, is considered and we also prove that MASs can achieve consensus in finite-time and asymptotically reach the optimal solution under the proposed protocol over directed networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results.

关键词： Multi-agent systems (MASs) distributed optimisation event-triggered finite-time aperiodically intermittent communication

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On the convergence of exact distributed generalisation and acceleration algorithm for convex optimisation

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2020年第16期51卷 3408-3424页

作者： Cheng, Huqiang Li, Huaqing Wang, Zheng Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing Peoples R China

In this paper, we study distributed multiagent optimisation over undirected graphs. The optimisation problem is to minimise a global objective function, which is composed of the sum of a set of local objective functions. Recent researches on this problem have made significant progress by using primal-dual methods. However, the inner link among different algorithms is unclear. This paper shows that some state-of-the-art algorithms differ in that they incorporate the slightly different last dual gradient terms based on the augmented Lagrangian analysis. Then, we propose a distributed Nesterov accelerated optimisation algorithm, where a doubly stochastic matrix is allowed to use, and nonidentical local step-sizes are employed. We analyse the convergence of the proposed algorithm by using the generalised small gain theorem under the assumption that each local objective function is strongly convex and has Lipschitz continuous gradient. We prove that the sequence generated by the proposed algorithm linearly converge to an optimal solution if the largest step-size is positive and less than an explicitly estimated upper bound, and the largest momentum parameter is nonnegative and less than an upper bound determined by the largest step-size. Simulation results further illustrate the efficacy of the proposed algorithm.

关键词： distributed optimisation Nesterov method acceleration nonidentical step-sizes small gain theorem linear convergence rate

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distributed bandit online optimisation for energy management in smart grids

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2023年第16期54卷 2957-2974页

作者： Zhao, Zhongyuan Xia, Lunchao Jiang, Luyao Ge, Quanbo Yu, Fang Nanjing Univ Informat Sci & Technol Coll Automat Nanjing 210044 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China China Elect Power Res Inst Nanjing Peoples R China

This paper presents a distributed optimisation algorithm based on one-point bandit feedback (OPBF) which enables the solving of energy management problems (EMPs) over directed networks. Unlike existing EMPs with known cost functions, the proposed online energy management approach considers a time-varying and unknown cost function, which creates sampling difficulty. To tackle this challenge, a random gradient-free oracle is constructed, allowing for the facilitation of output generation updates. This construction significantly mitigates the need for explicit expressions of the cost function. Furthermore, the proposed algorithm successfully enforces both the supply-demand balance constraint and the generation constraint in EMPs. In order to evaluate performance, this study introduces a performance index referred to as regret, which exhibits sublinear convergence. This finding provides additional evidence that the algorithm can achieve optimal output generation at a rapid convergence rate, subject to certain step-size conditions. Finally, the performance of the algorithm is verified on both a modified 6-bus system and an IEEE 162-bus system. The results demonstrate the effectiveness and efficiency of the proposed algorithm in solving EMPs over directed networks.

关键词： distributed optimisation one-point bandit randomised gradient-free oracles energy management

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