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  • 1.
    Du, Wen
    et al.
    Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
    Yi, Xinlei
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    George, Jemin
    US Army Res Lab, Adelphi, MD 20783 USA..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Yang, Tao
    Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
    Distributed Optimization with Dynamic Event-Triggered Mechanisms2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 969-974Conference paper (Refereed)
    Abstract [en]

    In this paper, we consider the distributed optimization problem, whose objective is to minimize the global objective function, which is the sum of local convex objective functions, by using local information exchange. To avoid continuous communication among the agents, we propose a distributed algorithm with a dynamic event-triggered communication mechanism. We show that the distributed algorithm with the dynamic event-triggered communication scheme converges to the global minimizer exponentially, if the underlying communication graph is undirected and connected. Moreover, we show that the event-triggered algorithm is free of Zeno behavior. For a particular case, we also explicitly characterize the lower bound for inter-event times. The theoretical results are illustrated by numerical simulations.

  • 2.
    George, Jemin
    et al.
    US Army Res Lab, Adelphi, MD 20783 USA..
    Yi, Xinlei
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Yang, Tao
    Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
    Distributed Robust Dynamic Average Consensus with Dynamic Event-Triggered Communication2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 434-439Conference paper (Refereed)
    Abstract [en]

    This paper presents the formulation and analysis of a fully distributed dynamic event-triggered communication based robust dynamic average consensus algorithm. Dynamic average consensus problem involves a networked set of agents estimating the time-varying average of dynamic reference signals locally available to individual agents. We propose an asymptotically stable solution to the dynamic average consensus problem that is robust to network disruptions. Since this robust algorithm requires continuous communication among agents, we introduce a novel dynamic event-triggered communication scheme to reduce the overall inter-agent communications. It is shown that the event-triggered algorithm is asymptotically stable and free of Zeno behavior. Numerical simulations are provided to illustrate the effectiveness of the proposed algorithm.

  • 3.
    Jafarian, Matin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Yi, Xinlei
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Pirani, Mohammad
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Synchronization of Kuramoto oscillators in a bidirectional frequency-dependent tree network2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 4505-4510Conference paper (Refereed)
    Abstract [en]

    This paper studies the synchronization of a finite number of Kuramoto oscillators in a frequency-dependent bidirectional tree network. We assume that the coupling strength of each link in each direction is equal to the product of a common coefficient and the exogenous frequency of its corresponding source oscillator. We derive a sufficient condition for the common coupling strength in order to guarantee frequency synchronization in tree networks. Moreover, we discuss the dependency of the obtained bound on both the graph structure and the way that exogenous frequencies are distributed. Further, we present an application of the obtained result by means of an event-triggered algorithm for achieving frequency synchronization in a star network assuming that the common coupling coefficient is given.

  • 4.
    Wei, Jieqiang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Yi, Xinlei
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Nonlinear Consensus Protocols With Applications to Quantized Communication and Actuation2019In: IEEE Transactions on Big Data, ISSN 2325-5870, E-ISSN 2168-6750, Vol. 6, no 2, p. 598-608Article in journal (Refereed)
    Abstract [en]

    Nonlinearities are present in all real applications. Two types of general nonlinear consensus protocols are considered in this paper, namely, the systems with nonlinear communication and actuator constraints. The solutions of the systems are understood in the sense of Filippov to handle the possible discontinuity of the controllers. For each case, we prove the asymptotic stability of the systems with minimal assumptions on the nonlinearity, for both directed and undirected graphs. These results extend the literature to more general nonlinear dynamics and topologies. As applications of established theorems, we interpret the results on quantized consensus protocols.

  • 5.
    Yang, Tao
    et al.
    Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
    Yi, Xinlei
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Wu, Junfeng
    Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China..
    Yuan, Ye
    Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Hubei, Peoples R China.;Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China..
    Wu, Di
    Pacific Northwest Natl Lab, Richland, WA 99352 USA..
    Meng, Ziyang
    Tsinghua Univ, Dept Precis Instrument, Beijing 100084, Peoples R China.;Tsinghua Univ, State Key Lab Precis Measurement Technol & Instru, Beijing 100084, Peoples R China..
    Hong, Yiguang
    Chinese Acad Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China..
    Wang, Hong
    Oak Ridge Natl Lab, Oak Ridge, TN 37932 USA..
    Lin, Zongli
    Univ Virginia, Charles L Brown Dept Elect & Comp Engn, Charlottesville, VA 22904 USA..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    A survey of distributed optimization2019In: Annual Reviews in Control, ISSN 1367-5788, E-ISSN 1872-9088, Vol. 47, p. 278-305Article, review/survey (Refereed)
    Abstract [en]

    In distributed optimization of multi-agent systems, agents cooperate to minimize a global function which is a sum of local objective functions. Motivated by applications including power systems, sensor networks, smart buildings, and smart manufacturing, various distributed optimization algorithms have been developed. In these algorithms, each agent performs local computation based on its own information and information received from its neighboring agents through the underlying communication network, so that the optimization problem can be solved in a distributed manner. This survey paper aims to offer a detailed overview of existing distributed optimization algorithms and their applications in power systems. More specifically, we first review discrete-time and continuous-time distributed optimization algorithms for undirected graphs. We then discuss how to extend these algorithms in various directions to handle more realistic scenarios. Finally, we focus on the application of distributed optimization in the optimal coordination of distributed energy resources. 

  • 6.
    Yi, Xinlei
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Liu, Kun
    Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China..
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Dynamic Event-Triggered and Self-Triggered Control for Multi-agent Systems2019In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 64, no 8, p. 3300-3307Article in journal (Refereed)
    Abstract [en]

    We propose two novel dynamic event-triggered control laws to solve the average consensus problem for first-order continuous-time multiagent systems over undirected graphs. Compared with the most existing triggering laws, the proposed laws involve internal dynamic variables, which play an essential role in guaranteeing that the triggering time sequence does not exhibit Zeno behavior. Moreover, some existing triggering laws are special cases of ours. For the proposed self-triggered algorithm, continuous agent listening is avoided as each agent predicts its next triggering time and broadcasts it to its neighbors at the current triggering time. Thus, each agent only needs to sense and broadcast at its triggering times, and to listen to and receive incoming information from its neighbors at their triggering times. It is proved that the proposed triggering laws make the state of each agent converge exponentially to the average of the agents' initial states if and only if the underlying graph is connected. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

  • 7.
    Yi, Xinlei
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China..
    Lu, Wenlian
    Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China.;Fudan Univ, Ctr Computat Syst Biol, Shanghai 200433, Peoples R China..
    Chen, Tianping
    Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China.;Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China..
    Centralized Event-triggered Control for Linear Multi-agent Systems2016In: PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC), IEEE , 2016, p. 225-230Conference paper (Refereed)
    Abstract [en]

    In this paper, we study the consensus problem of linear multi-agent systems. Centralized event-triggered rules are provided so as to reduce the frequency of system's updating. The diffusion coupling feedbacks of each agent are based on the latest observations from its in-neighbours and the system's next observation time is triggered by a criterion based on all agents' information. The scenario of continuous monitoring is first considered, namely all agents' instantaneous states can be observed. It is proved that if the network topology is connected, then this centralized event-triggered coupling strategy can realize consensus exponentially for the linear multi-agent systems. Then the results are extended to discontinuous monitoring, where the system computes its next triggering time in advance without having to observe all agents' states continuously. As a special case, we apply above results to double-integrator consensus problem. One example with numerical simulation are provided to show the effectiveness of the theoretical results.

  • 8.
    Yi, Xinlei
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Yao, Lisha
    Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
    Yang, Tao
    Univ North Texas, Dept Elect Engn, Denton, TX 76203 USA..
    George, Jemin
    US Army Res Lab, Adelphi, MD 20783 USA..
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 3397-3402Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a fully distributed algorithm for second-order continuous-time multi-agent systems to solve the distributed optimization problem. The global objective function is a sum of private cost functions associated with the individual agents and the interaction between agents is described by a weighted undirected graph. We show the exponential convergence of the proposed algorithm if the underlying graph is connected, each private cost function is locally gradient-Lipschitz- continuous, and the global objective function is restricted strongly convex with respect to the global minimizer. Moreover, to reduce the overall need of communication, we then propose a dynamic event-triggered communication mechanism that is free of Zeno behavior. It is shown that the exponential convergence is achieved if the private cost functions are also globally gradient-Lipschitz- continuous. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

1 - 8 of 8
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