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distributed optimization Framework for Shadow Removal in Multi-Projection Systems

COMPUTER GRAPHICS FORUM 2017年第8期36卷 369-379页

作者： Tsukamoto, J. Iwai, D. Kashima, K. Grad Sch Informat Kyoto Japan Grad Sch Engn Sci Toyonaka Osaka Japan

This paper proposes a novel shadow removal technique for cooperative projection system based on spatiotemporal prediction. In our previous work, we proposed a distributed feedback algorithm, which is implementable in cooperative projection environments subject to data transfer constraints between components. A weakness of this scheme is that the compensation is conducted in each pixel independently. As a result, spatiotemporal information of the environmental change cannot be utilized even if it is available. In view of this, we specifically investigate the situation where some of the projectors are occluded by a moving object whose one-frame-ahead behaviour is predictable. In order to remove the resulting shadow, we propose a novel error propagating scheme that is still implementable in a distributed manner and enables us to incorporate the prediction information of the obstacle. It is demonstrated theoretically and experimentally that the proposed method significantly improves the shadow removal performance in comparison to the previous work.

关键词： spatial augmented reality projection mapping mutli-projection system shadow removal control theory distributed optimization

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distributed optimization for Second-Order Discrete-Time Multiagent Systems With Set Constraints

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第9期34卷 5629-5639页

作者： Zou, Yao Xia, Kewei Huang, Bomin Meng, Ziyang Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China Tsinghua Univ Dept Precis Instrument Beijing 100084 Peoples R China

The optimization problem of second-order discrete-time multiagent systems with set constraints is studied in this article. In particular, the involved agents cooperatively search an optimal solution of a global objective function summed by multiple local ones within the intersection of multiple constrained sets. We also consider that each pair of local objective function and constrained set is exclusively accessible to the respective agent, and each agent just interacts with its local neighbors. By borrowing from the consensus idea, a projection-based distributed optimization algorithm resorting to an auxiliary dynamics is first proposed without interacting the gradient information of local objective functions. Next, by considering the local objective functions being strongly convex, selection criteria of step size and algorithm parameter are built such that the unique solution to the concerned optimization problem is obtained. Moreover, by fixing a unit step size, it is also shown that the optimization result can be relaxed to the case with just convex local objective functions given a properly chosen algorithm parameter. Finally, practical and numerical examples are taken to verify the proposed optimization results.

关键词： optimization Heuristic algorithms Linear programming Multi-agent systems Convergence Topology distributed algorithms Consensus convex functions discrete-time systems distributed optimization multiagent systems

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distributed optimization for a class of uncertain nonlinear multi-agent systems with arbitrary relative degree subject to exogenous disturbances

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第8期32卷 4631-4647页

作者： Liu, Puzhao Li, Ranran Northeastern Univ Coll Informat Sci & Engn 11Lane 3Wenhua Rd Shenyang 110819 Peoples R China

This paper studies the distributed optimization problem on the framework of a class of arbitrary relative degree uncertain nonlinear multi-agent systems with the presence of exogenous disturbances. Based on the internal model principle and the pseudo gradient approach, a novel continuous-time distributed optimization control protocol is proposed to make all the agents reach optimal consensus by utilizing local cost functions information and neighbors' states information. The main advantage of our algorithm is that it can run on systems with more complex dynamics, while the existing algorithms apply only to integral-type systems or unity relative degree nonlinear systems. Moreover, the convergence of the algorithm is proved by Lyapunov function and graph theory analysis. Finally, the distributed protocol is physically implemented by circuits and tested on a group of Chua's circuit systems to show the effectiveness of the theoretical results.

关键词： arbitrary relative degree distributed optimization exogenous disturbances internal model principle uncertain nonlinear multi-agent systems

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distributed optimization Based on a Multiagent System in the Presence of Communication Delays

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2017年第5期47卷 717-728页

作者： Yang, Shaofu Liu, Qingshan Wang, Jun Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Hong Kong Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China City Univ Hong Kong Dept Comp Sci Computat Intelligence Hong Kong Hong Kong Peoples R China

In this paper, distributed optimization is addressed based on a continuous-time multiagent system in the presence of time-varying communication delays. First, the relationship between optimal solutions and the equilibrium points of the multiagent system with time delay is revealed. Next, delay-dependent and delay-independent sufficient conditions in form of linear matrix inequality are derived for ascertaining convergence to optimal solutions, in the cases of slow-varying delay and fast-varying delay. Furthermore, a set of conditions are also obtained for the delay-free case. In addition, a sampled-data communication scheme is presented based on the conditions for the fast varying delay systems. Simulation results are presented to substantiate the theoretical results. An application for distributed parameter estimation is also given.

关键词： Communication delays consensus distributed optimization multiagent systems

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distributed optimization With Projection-Free Dynamics: A Frank-Wolfe Perspective

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第1期54卷 599-610页

作者： Chen, Guanpu Yi, Peng Hong, Yiguang Chen, Jie KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-11428 Stockholm Sweden Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200092 Peoples R China

We consider solving distributed constrained optimization in this article. To avoid projection operations due to constraints in the scenario with large-scale variable dimensions, we propose distributed projection-free dynamics by employing the Frank-Wolfe method, also known as the conditional gradient. Technically, we find a feasible descent direction by solving an alternative linear suboptimization. To make the approach available over multiagent networks with weight-balanced digraphs, we design dynamics to simultaneously achieve both the consensus of local decision variables and the global gradient tracking of auxiliary variables. Then, we present the rigorous convergence analysis of the continuous-time dynamical systems. Also, we derive its discrete-time scheme with an accordingly proved convergence rate of $O(1/k)$ . Furthermore, to clarify the advantage of our proposed distributed projection-free dynamics, we make detailed discussions and comparisons with both existing distributed projection-based dynamics and other distributed Frank-Wolfe algorithms.

关键词： Constraint distributed optimization Frank-Wolfe projection free

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distributed optimization of multi-agent systems with delayed sampled-data

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NEUROCOMPUTING 2018年 296卷 100-108页

作者： Yan, Junxiu Yu, Hui Xia, Xiaohua China Three Gorges Univ Coll Sci Yichang 443002 Peoples R China China Three Gorges Univ Three Gorges Math Res Ctr Yichang 443002 Peoples R China Univ Pretoria Dept Elect Elect & Comp Engn Ctr New Energy Syst ZA-0002 Pretoria South Africa

In this paper, we study the distributed optimization problem of multi-agent systems with delayed sampled-data, where the interconnected topology is directed, weighted-balanced and strongly connected, and also local cost functions are strongly convex with globally Lipschitz gradients. Based on synchronous and asynchronous sampled-data, we construct two respective algorithms. Our main results, sufficient conditions for the convergence to an optimal solution, are obtained under assumption that all design parameters are chosen properly. We also present one example to validate our theoretical results. (C) 2018 Elsevier B.V. All rights reserved.

关键词： distributed optimization Consensus Sampled-data Multi-agent system

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distributed optimization and Coordination Algorithms for Dynamic Traffic Metering in Urban Street Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2019年第5期20卷 1930-1941页

作者： Mohebifard, Rasool Hajbabaie, Ali Washington State Univ Dept Civil & Environm Engn Pullman WA 99164 USA

Previous research has shown that proper metering of entry traffic to urban street networks, similar to metering traffic on on-ramps in freeway facilities, reduces traffic congestion, especially in oversaturated flow conditions. Building on the previous research, this paper presents a real-time and scalable methodology for finding near-optimal metering rates dynamically in urban street networks. The problem is formulated into a mixed-integer linear program (MILP) based on the cell transmission model. We propose a distributed optimization scheme that decomposes the network level MILP into several link-level MILPs to reduce the complexity of the problem. We convert the link-level MILPs to linear programs to reduce the computational complexity further. Moreover, we create distributed coordination between the link-level linear programs to push the solutions toward optimality. The distributed optimization and coordination solution algorithm is incorporated into a rolling horizon technique to account for the time-varying demand and capacity and to reduce the computational complexity further. We applied the proposed solution technique to a number of case studies and observed that it was scalable and real time and found solutions that were at most 2.2% different from the optimal solution of the problem. Like the previous studies, we found significant improvements in network operations as a result of traffic metering.

关键词： Dynamic traffic metering perimeter control distributed optimization distributed coordination model predictive control mixed-integer linear program

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distributed optimization of Disturbed Nonlinear Multi-Agent Systems via Adaptive Fault-Tolerant Output Regulation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2023年第12期70卷 4479-4483页

作者： Kang, Jian Guo, Ge 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

This brief considers a distributed fault-tolerant optimization problem of disturbed nonlinear multi-agent systems, which is converted to an output regulation issue of tracking an exosystem with unknown states. To solve this problem, a new adaptive fault-tolerant controller including exponential terms is yielded, with giving two fundamental design conditions of the exosystem state observer to achieve asymptotic or exponential convergence and compensate for unbounded external disturbances. The proposed exosystem observer-based adaptive fault-tolerant output regulation approach supplies a common distributed optimization or cooperative control framework for faulty nonlinear multi-agent systems. A simulation example is presented to verify the effectiveness.

关键词： distributed optimization nonlinear systems adaptive fault-tolerant control exponential convergence

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distributed optimization for Network Resource Allocation With Nonsmooth Utility Functions

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2019年第4期6卷 1354-1365页

作者： Iiduka, Hideaki Meiji Univ Dept Comp Sci Kawasaki Kanagawa 2148571 Japan

The network utility maximization problem is the problem of maximizing the overall utility of a network under capacity constraints, where each source in the network has its own private nonsmooth concave utility function (which allows the true utility to be modeled accurately) and each link in the network has only its capacity constraint. To solve this problem, two distributed optimization algorithms are proposed: a projected proximal algorithm and a projected subgradient algorithm. These algorithms can be implemented for the case that each source tries to maximize only its utility by using its proximity operator or subdifferential and each link tries to satisfy only its capacity constraint by using the metric projection onto its capacity constraint set. A convergence analysis indicates that these algorithms are sufficient for each source to find the optimal resource allocation. The convergence, optimality, and performance of the proposed algorithms are demonstrated through numerical comparisons with the existing decentralized network flow control algorithm.

关键词： distributed optimization metric projection network utility maximization (NUM) nonsmooth utility function proximity operator subdifferential

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distributed optimization in Heterogeneous Dynamical Networks

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第1期44卷 473-483页

作者： Adibzadeh, Amir Suratgar, Amir A. Menhaj, Mohammad B. Zamani, Mohsen Amirkabir Univ Technol Elect Engn Dept Tehran 15914 Iran Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia

In this paper, distributed optimization in heterogeneous dynamical networks that consist of both single- and double-integrator agents are studied. This paper is divided into two main parts. In the first part, all agents shall reach a consensus point that minimizes the sum of local convex objective functions. In the proposed solution, it is assumed that the local objective functions are only available to their associated agents. These agents have access to the states information of their neighbors through a communication protocol. These agents admit no constraints. In the second part, we will tackle the same distributed optimization problem as the one in the first part;however, we assume that each agent is subject to a local decoupled constraint set. To solve this problem, we will adopt an embedded control scheme in which each agent integrates a virtual unit that cooperates with those of neighboring agents to produce proper reference signals for the original dynamics. These reference signals conduct the agents toward the globally optimal point. Then, we design decentralized feedback controllers to make the agents track the reference signals. Stability analysis and convergence proof are given for the both parts. At the end, we simulate the proposed approaches on a group of heterogeneous wheeled robots.

关键词： Convex optimization distributed optimization Heterogeneous networks Virtual embedded dynamics

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