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distributed Estimation in Multimissile Cyber-Physical Systems With Time Delay

IEEE SYSTEMS JOURNAL 2020年第1期14卷 1491-1502页

作者： Abdelmawgoud, Ahmed Jamshidi, Mo Benavidez, Patrick Raytheon Missile Syst Syst Design & Performance Directorate Tucson AZ 85706 USA Univ Texas San Antonio Coll Engn San Antonio TX 78249 USA

In this article a widely studied consensus algorithm for processing sensor data of multimissile systems is considered. A novelty of algorithms to reach a consensus given time-delay constraints is introduced. In previous work, the author proposed an algorithm addressing the same problem. The primary goal of this article is to evaluate the performance of the proposed algorithm. The performance was evaluated in Monte Carlo runs in a target tracking scenario. A mission with multimissile trying to intercept a single flying threat is assumed in this scenario. The interceptors are using nonhomogeneous sensors to measure the location of the threat continuously. Each missile computes its state prediction and shares it with its neighboring missile only. However, the shared information is applied to arbitrary nonuniform time delay. The entire group of missiles must reach a consensus on the target's location. The performance is evaluated in terms of overall estimation error, conflict resolution, and time to reach a consensus. Different scenarios are also simulated to examine the effectiveness of the number of missiles performing observation, the delay value, and sensor uncertainty in the overall system stability. Moreover, the tradeoffs of using the algorithm are identified.

关键词： Delayed systems distributed algorithms kalman filters missiles multi-agent systems state estimation unmanned aerial vehicles

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distributed Constrained Online Learning

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 3486-3499页

作者： Paternain, Santiago Lee, Soomin Zavlanos, Michael M. Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA Yahoo Res Verizon Media Sunnyvale CA 94089 USA Duke Univ Dept Mech Engn & Mat Sci Durham NC 27708 USA

In this article, we consider groups of agents in a network that select actions in order to satisfy a set of constraints that vary arbitrarily over time and minimize a time varying function of which they have only local observations. The selection of actions, also called a strategy, is causal and decentralized, i.e., the dynamical system that determines the actions of a given agent depends only on the constraints at the current time and on its own actions and those of its neighbors. To determine such a strategy, we propose a decentralized saddle point algorithm and showthat the corresponding global fit and regret are bounded by functions of the order of root T i.e., functions whose limit is a constant when divided by root T. Specifically, we define the global fit of a strategy as a vector that integrates over time the global constraint violations as seen by a given node. The fit is a performance loss associated with online operation as opposed to offline clairvoyant operation which can always select an action, if one exists, that satisfies the constraints at all times. If this fit grows sublinearly with the time horizon it suggests that the strategy approaches the feasible set of actions. Likewise, we define the regret of a strategy as the difference between its accumulated cost and that of the best fixed action that one could select knowing beforehand the time evolution of the objective function. Numerical examples support the theoretical conclusions.

关键词： distributed algorithms gradient methods learning (artificial intelligence)

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distributed event-triggered algorithm for optimal resource allocation of second-order multi-agent systems

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IET CONTROL THEORY AND APPLICATIONS 2020年第14期14卷 1937-1946页

作者： Deng, Zhenhua Wang, Lei Cent South Univ Sch Automat Changsha 410083 Peoples R China

In this work, the authors study the resource allocation problems of second-order multi-agent systems, where each agent is associated with a local cost function and the decisions of agents are constrained by network resource constraints. To seek the optimal resource allocation with low communication costs, a distributed algorithm with event-triggered schemes is proposed. With the help of Lyapunov stability theory and convex analysis, the authors analyse the convergence of the algorithm and prove the event-triggered schemes are free of Zeno behaviour. Under the proposed algorithm, the second-order agents exponentially converge to the exact optimal decision of the resource allocation problem, and the burden of communication is reduced effectively. Finally, a simulation of the economic dispatch problem is given to illustrate the effectiveness of the authors' results.

关键词： multi-robot systems multi-agent systems distributed algorithms resource allocation distributed control stability Lyapunov methods distributed event-triggered algorithm optimal resource allocation second-order multiagent systems resource allocation problem local cost function network resource constraints low communication costs distributed algorithm event-triggered schemes second-order agents exact optimal decision

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Utility Design for distributed Resource Allocation-Part I: Characterizing and Optimizing the Exact Price of Anarchy

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第11期65卷 4616-4631页

作者： Paccagnan, Dario Chandan, Rahul Marden, Jason R. UC Santa Barbara Mech Engn Dept Santa Barbara CA 93106 USA UC Santa Barbara Ctr Control Dynam Syst & Computat Santa Barbara CA 93106 USA Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

Game theory has emerged as a fruitful paradigm for the design of networked multiagent systems. A fundamental component of this approach is the design of agents' utility functions so that their self-interested maximization results in a desirable collective behavior. In this article, we focus on a well-studied class of distributed resource allocation problems, where each agent is requested to select a subset of resources with the goal of optimizing a given system-level objective. Our core contribution is the development of a novel framework to tightly characterize the worst-case performance of any resulting Nash equilibrium (price of anarchy) as a function of the chosen agents' utility functions. Leveraging this result, we identify how to design such utilities so as to optimize the price of anarchy through a tractable linear program. This provides us with a priori performance certificates applicable to any existing learning algorithm capable of driving the system to an equilibrium. Part II of this article specializes these results to submodular and supermodular objectives, discusses the complexity of computing Nash equilibria, and provides multiple illustrations of the theoretical findings.

关键词： Resource management Games Optimization Nash equilibrium distributed algorithms Combinatorial optimization distributed optimization game theory price of anarchy resource allocation

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distributed gradient-based sampling algorithm for least-squares in switching multi-agent networks

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Science China(Information Sciences) 2020年第9期63卷 284-293页

作者： Peng LIN Hongsheng QI Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

Dear editor,Recent years have witnessed a rapid growth of distributed design in multi-agent networks because of the scalability, robustness and low cost. Compared with the conventional centralized and parallel design, all agents in fully distributed design aim to achieve the global goal only based on the local measurement and information sharing with

关键词： multi-agent systems distributed algorithms least-squares gradient-based sampling time-varying networks

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Local Graph Clustering With Network Lasso

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 106-110页

作者： Jung, Alexander SarcheshmehPour, Yasmin Aalto Univ Dept Comp Sci Espoo 02150 Finland Sharif Univ Technol Dept Math Sci Tehran *** Iran

We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via network flows between cluster boundaries and seed nodes. While spectral clustering methods are guided by a minimization of the graph Laplacian quadratic form, nLasso minimizes the total variation of cluster indicator signals. As demonstrated theoretically and numerically, nLasso methods can handle very sparse clusters (chain-like) which are difficult for spectral clustering. We also verify that a primal-dual method for non-smooth optimization allows to approximate nLasso solutions with optimal worst-case convergence rate.

关键词： TV Clustering methods Optimization Minimization Laplace equations Message passing Convergence Clustering algorithms distributed algorithms flow graphs machine learning network theory (graphs) semisupervised learning

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Finite-Time distributed Linear Equation Solver for Solutions With Minimum l1-Norm

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第4期65卷 1691-1696页

作者： Zhou, Jingqiu Wang, Xuan Mou, Shaoshuai Anderson, Brian D. O. Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47906 USA Hangzhou Dianzi Univ Hangzhou 310018 Peoples R China Australian Natl Univ Data 61 CSIRO Canberra ACT 2600 Australia

This paper proposes a continuous-time distributed algorithm for multiagent networks to achieve a solution with the minimum l(1)-norm to underdetermined linear equations. The proposed algorithm comes from a combination of the Filippov set-valued map with the projection-consensus flow. Given the underlying network is undirected and fixed, it is shown that the proposed algorithm drives all agents' individual states to converge in finite time to a common value, which is the minimum l(1)-norm solution.

关键词： distributed algorithms finite-time convergence minimum l(1)-norm solutions

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distributed multi-agent optimisation via coordination with second-order nearest neighbours

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IET CONTROL THEORY AND APPLICATIONS 2020年第13期14卷 1733-1743页

作者： Ren, Xiaoxing Li, Dewei Xi, Yugeng Shao, Haibin Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Dept Automat Shanghai 200240 Peoples R China

The authors examine the distributed multi-agent optimisation problem allowing each agent to explicitly utilise the information of its first-order and second-order nearest neighbours for cooperative decision-making. Specifically, at each time instance, each agent in the network employs the states of its second-order nearest neighbours at the last time instance, which can be transmitted from its first-order nearest neighbours in practice. Under the proposed framework, they propose a distributed subgradient algorithm asymptotically leading all agents to an agreement on the optimal solution of the multi-agent optimisation problem under an appropriate selection of stepsize. Furthermore, they show that the proposed algorithm outperforms the existing algorithms built on the assumption that only the states of first-order neighbours are available in terms of the convergence rate.

关键词： multi-agent systems optimisation gradient methods nearest neighbour methods convergence distributed algorithms first-order nearest neighbours distributed subgradient algorithm second-order nearest neighbours distributed multiagent optimisation problem time instance cooperative decision-making convergence rate

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Asynchronous distributed Power Control of Multimicrogrid Systems

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第4期7卷 1960-1973页

作者： Wang, Zhaojian Chen, Laijun Liu, Feng Yi, Peng Cao, Ming Deng, Sicheng Mei, Shengwei Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Dept Automat Shanghai 200240 Peoples R China Tsinghua Univ State Key Lab Power Syst Dept Elect Engn Beijing 100084 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Dept Control Sci & Engn Shanghai 201804 Peoples R China Univ Groningen Fac Sci & Engn NL-9747 AG Groningen Netherlands

Asynchrony widely exists in microgrids (MGs), such as nonidentical sampling rates and communication delays, which challenges the MG control. This article addresses the asynchronous distributed power control problem of hybrid microgrids, considering different kinds of asynchrony, such as nonidentical sampling rates, and random time delays. To this end, we first formulate the economic dispatch problem of MGs, and devise a synchronous algorithm. Then, we analyze the impact of asynchrony, and propose an asynchronous iteration algorithm based on the synchronous version. By introducing a random clock at each iteration, different types of asynchrony are fitted into a unified framework, where the asynchronous algorithm is converted into a fixed-point iteration problem with a nonexpansive operator, leading to a convergence proof. We further provide an upper bound estimation of the time delay. Moreover, the real-time implementation of the proposed algorithm in both ac and dc MGs is introduced. By measuring the frequency/voltage, the controller is simplified by reducing one order, and adapting to the fast varying load demand. Finally, simulations on a benchmark MG, and experiments are utilized to verify the effectiveness, and advantages of the proposed algorithm.

关键词： Convergence Delay effects distributed algorithms Economics Frequency measurement Real-time systems Control systems Asynchronous control distributed control multiagent system multimicrogrid networks time delay

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distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

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PROCEEDINGS OF THE IEEE 2020年第11期108卷 1953-1967页

作者： Jaleel, Hassan Shamma, Jeff S. Lahore Univ Management Sci LUMS Intelligent Machines & Sociotech Syst iMaSS Lab Dept Elect Engn Syed Babar Ali Sch Sci & Engn Lahore 54792 Pakistan King Abdullah Univ Sci & Technol KAUST Robot Intelligent Syst & Control RISC Lab Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia

Recent advances in sensing, communication, and computing technologies have enabled the use of multirobot systems for practical applications such as surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time-implementable, require local information only, and guarantee some desired global performance. distributed optimization provides a framework for designing such local decision-making rules for multirobot systems. In this article, we present a collection of selected results for distributed optimization for robot networks. We will focus on two special classes of problems: 1) real-time path planning for multirobot systems and 2) self-organization in multirobot systems using game-theoretic approaches. For multirobot path planning, we will present some recent approaches that are based on approximately solving distributed optimization problems over continuous and discrete domains of actions. The main idea underlying these approaches is that a variety of path planning problems can be formulated as convex optimization and submodular minimization problems over continuous and discrete action spaces, respectively. To generate local update rules that are efficiently implementable in real time, these approaches rely on approximate solutions to the global problems that can still guarantee some level of desired global performance. For game-theoretic self-organization, we will present a sampling of results for area coverage and real-time target assignment. In these results, the problems are formulated as games, and online updating rules are designed to enable teams of robots to achieve the collective objective in a distributed manner.

关键词： Optimization Robot kinematics Convex functions Task analysis Real-time systems Multi-robot systems Convex functions distributed algorithms multi-robot systems optimization

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