Article
Robotics
Kai Matsuka, Soon-Jo Chung
Summary: In this article, new algorithms for distributed factor graph optimization (DFGO) problems in large-scale networked robotic systems are presented. The algorithms include a batch DFGO algorithm called local consensus ADMM (LC-ADMM) and a real-time DFGO algorithm called incremental DFGO (iDFGO). The LC-ADMM is fully localized and has exponential convergence for strongly convex objectives, while the iDFGO algorithm is scalable with respect to both network size and time.
IEEE TRANSACTIONS ON ROBOTICS
(2023)
Article
Automation & Control Systems
Xuan Wang, Shaoshuai Mou, Brian D. O. Anderson
Summary: Inspired by integral feedback, this study proposes a distributed constant gain algorithm for multiagent networks to solve convex optimization problems with local linear constraints. Utilizing an undirected graph model for agent interactions, the algorithm achieves the optimum solution with an exponential convergence rate. The algorithm inherits beneficial requirements on communication bandwidth and robustness against disturbance from integral feedback. Analytical proof and numerical simulations validate the effectiveness of the proposed algorithm in solving constrained optimization problems.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Chao Huang, Brian D. O. Anderson, Hao Zhang, Huaicheng Yan
Summary: For a group of networked agents, f-consensus means reaching a consensus on the value of a desired function, f, based on the initial state of the individual agents. This paper demonstrates how f-consensus problems can often be converted into distributed convex optimization (DCO) problems, which can be easily solved using existing DCO algorithms. This approach offers computational advantages and can solve specific classes of f-consensus problems, including weighted power mean consensus and kth smallest value or kth order statistic consensus.
Article
Automation & Control Systems
Guido Carnevale, Francesco Farina, Ivano Notarnicola, Giuseppe Notarstefano
Summary: This article presents a network of computing agents that aims to solve an online optimization problem in a distributed manner, without the need for a central coordinator. The proposed GTAdam algorithm combines a gradient tracking mechanism with first- and second-order momentum estimates of the gradient. It is analyzed in the online setting for strongly convex cost functions with Lipschitz continuous gradients and is found to outperform state-of-the-art distributed optimization methods in numerical experiments.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Automation & Control Systems
Liwei An, Guang-Hong Yang
Summary: This article studies the problem of distributed optimal coordination for heterogeneous linear multiagent systems and proposes a new algorithm to solve this problem. The algorithm has strong generality and convergence, achieving global asymptotical convergence by utilizing only local interaction.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Dewen Li, Ning Li, Frank Lewis
Summary: The novel generalized constrained convex optimization model introduced unifies traditional optimization, resource allocation, and economic dispatch problems. By requiring only the global objective function to be convex, it simplifies the condition for individual agents. Introducing the generalized Lagrange multiplier method avoids the need for positive projections in the presence of inequality constraints, resulting in smooth dynamics and a continuous Lyapunov derivative.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2021)
Article
Automation & Control Systems
Naoki Hayashi
Summary: This article discusses a distributed method for constrained convex optimization over open multiagent networks. The method takes into account the joining and leaving of agents in an open multiagent system, as well as the problem of active agents finding the optimal strategy in a finite-time horizon. The estimation is updated using a subgradient-based distributed algorithm, and the performance is evaluated using regret.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Hadi Reisizadeh, Behrouz Touri, Soheil Mohajer
Summary: In this article, we study strongly convex distributed optimization problems where a group of agents aim to collaboratively solve a separable optimization problem. We propose and analyze a decentralized gradient descent algorithm that considers the lossy sharing of information over time-varying graphs. By assuming appropriate step-size sequences, connectivity conditions, and bounded gradients, we demonstrate the convergence of agents' estimates to the optimal solution with a rate of O(T-1/2). Furthermore, we introduce novel tools for studying distributed optimization with diminishing averaging weights over time-varying graphs.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Lifeng Zheng, Liang Ran, Huaqing Li, Liping Feng, Zheng Wang, Qingguo Lu, Dawen Xia
Summary: This paper focuses on a class of decentralized convex optimization problems and introduces a synchronous full-decentralized primal-dual proximal splitting algorithm and its randomized version. The problems are solved through local information exchange without global coordination, and the convergence results are obtained with the help of asymmetric forward-backward-adjoint splitting technique. Numerical simulations demonstrate the effectiveness and practicability of the algorithms.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Haris E. Psillakis, Konstantinos A. Oikonomidis
Summary: This paper proposes a new framework for solving the optimal consensus problem of agents with continuous dynamics. By introducing novel auxiliary continuous variables and using suitable smoothing functions, the continuity of the variables is guaranteed. It also designs adaptive fuzzy distributed controllers to approximate the unknown system nonlinearities and ensure optimal consensus.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mashrur Rashik, Md Musfiqur Rahman, Md Mosaddek Khan, Md Mamun-or-Rashid, Long Tran-Thanh, Nicholas R. Jennings
Summary: The DPOP algorithm, while effective in solving DCOPs in cooperative multi-agent systems, faces scalability issues due to the lack of consideration for hard constraints in traditional DCOP formulations. To address this, a new algorithm is developed which improves message exchange paths and introduces cross-edge consistency for constraint propagation, resulting in significant runtime reductions compared to existing state-of-the-art algorithms.
APPLIED INTELLIGENCE
(2021)
Article
Automation & Control Systems
Zhiqiang Zhang, Jan Lunze, Yuangong Sun, Zehuan Lu
Summary: This article presents distributed continuous-time algorithms with dynamic event-triggered communication to solve a convex optimization problem in a multiagent network, and demonstrates their effectiveness through numerical simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Cong Wang, Shengyuan Xu, Deming Yuan, Baoyong Zhang, Zhengqiang Zhang
Summary: This article discusses the distributed convex optimization problem over a multiagent system and investigates the impact of communication delays on the convergence results of the PS-DDA algorithm. The performance of the PS-DDA algorithm is demonstrated through numerical simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Zhongguo Li, Zhengtao Ding
Summary: This article formulates a distributed multiobjective optimization problem for resource allocation in network-connected multiagent systems. Novel distributed algorithms are proposed to solve the problem using the weighted L-p preference index without specifying unknown parameters. The framework does not require prior information and can protect private data effectively using a distributed approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Keito Inoue, Naoki Hayashi, Shigemasa Takai
Summary: This paper investigates distributed online optimization with dynamic inequality constraints under time-varying communication delays. A group of agents cooperatively estimate an optimal strategy by exchanging sequentially disclosed loss value information. The authors propose a distributed primal-dual algorithm for an enlarged multiagent network with delayed agents, which handles the delayed information. Theoretical analysis shows that the algorithm achieves sublinear bounds for both the dynamic regret and fit functions, even in the presence of communication delays. Numerical examples validate the sublinearity of the proposed method.
