Dynamic Event-Triggered and Self-Triggered Control for Multi-agent Systems

Yi, Xinlei; Liu, Kun; Dimarogonas, Dimos V.; Johansson, Karl Henrik

doi:10.1109/tac.2018.2874703

Cited by 368 publications

(116 citation statements)

References 26 publications

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“…This paper involves coordinated optimization with switching strategy to avoid potential risk in a positive and co-ordinated way, event-triggered mechanism is proposed for positive action from power supply and load demand side. Generally, event-triggered coordination approach can be considered as control theory with network communication [19], [20], [21], [22], [23], [24]. Though literature [24] has been successfully implemented in power system, event-triggered strategy still depends network communication.…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Multiagent Optimization for Two-Layered Model of Hybrid Energy System With Price Bidding-Based Demand Response

Zhang

Yue

Dou

et al. 2021

IEEE Trans. Cybern.

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Due to uncertainty and dynamic characteristics from intermittent energy and load demand response (DR), it brings great challenge to optimal operation of hybrid energy system. This paper proposes an event-triggered multi-agent coordinated optimization strategy with two-layered architecture. Firstly, price-bidding based DR model is proposed with different stakeholders, and it also deduces optimal bidding price with Nash equilibrium theory. Then, four agents are designed to control different kind of energy resources, Agent 1 mainly analyzes the uncertainty or randomness caused by intermittent power, Agent 2 takes charge of dynamic economic dispatch (DED) within thermal units, Agent 3 manages the optimal scheduling of energy storage, and Agent 4 mainly undertakes load shifting strategy from consumers. In the upperlayer level, all agents coordinate together to ensure the stability of hybrid energy system with event-triggered mechanism, the intelligent control approach mainly depends on switching on/off power generators or curtailing system load, and consensus algorithm is utilized to optimize subsystem problem in lowerlayer level. Furthermore, simulation results can further verify the efficiency of proposed method, and it also reveals that eventtriggered multi-agent optimization strategy can be a promising way for solving hybrid energy system problem. Index Terms-event-triggered, coordinated optimization, demand response, intelligent control, hybrid energy system.

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Section: Introductionmentioning

confidence: 99%

Event-Triggered Multiagent Optimization for Two-Layered Model of Hybrid Energy System With Price Bidding-Based Demand Response

Zhang

Yue

Dou

et al. 2021

IEEE Trans. Cybern.

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“…However, in practical applications, the agents are usually equipped with digital microprocessor, resulting in limited energy storage and communication resources. In order to avoid excessive information transmission and reduce the consumption of communication resources, an effective method is to use event‐triggered strategies to determine the updates of the controller 15‐34 . Dimarogonas et al 16 presented centralized and distributed event‐triggered strategies to ensure consensus of first‐order model and generalize the results to self‐triggered case.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered mean square consensus of linear multiagent systems with measurement noises

2020

Adaptive Control & Signal

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Summary This article investigates mean square consensus of linear multiagent systems with measurement noises based on event‐triggered control. Considering various uncertain factors in the communication environment, multiplicative noises exists in the information that each agent measures from its neighbors. In order to save limited resources and to exclude the Zeno phenomenon, the update of the controller for each agent is driven by properly defined event‐triggered condition. Sufficient conditions are given to ensure exponential mean square consensus of multiagent systems with multiplicative noises by utilizing static and dynamic event‐triggered scheme. The interevent times of static event‐triggered mechanism is less than those of corresponding dynamic case for each agent. Finally, validity of the theoretical results is illustrated through a simulation example.

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“…During the past two decades, much attention has been focused on the research of cooperative control for its extensive applications in various fields, such as coordination and control of distributed sensor networks, satellite clusters, unmanned‐air‐vehicle formations, teaming of multirobotics, and elsewhere . Many control techniques based on continuous control have been developed for synchronization and consensus of multiagent networks, such as event‐triggered control, observer‐based protocol, adaptive control, and finite‐time control .…”

Section: Introductionmentioning

confidence: 99%

Stochastic synchronization of delayed multiagent networks with intermittent communications: An impulsive framework

Cui

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper investigates the stochastic synchronization problem of delayed multiagent networks with intermittent communications. Two kinds of impulsive effects are taken into account, ie, impulsive controller (positive impulsive effect) and impulsive disturbance (negative impulsive effect). Impulsive controller allows the synchronization to be realized and requires only state information exchange at discrete time instants such that the communication cost of bandwidth is reduced. Meanwhile, impulsive disturbance is inevitable in most of practical systems and therefore is taken into consideration at discrete time instants. Sufficient conditions for synchronization are given in terms of the graph topology, the control coupling gains, and the individual agent dynamics parameters, which indicates that synchronization can be realized if the impulsive effects coefficients and communication rate are suitably selected. Simulation results verify the effectiveness of the proposed synchronization protocol.

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Dynamic Event-Triggered and Self-Triggered Control for Multi-agent Systems

Cited by 368 publications

References 26 publications

Event-Triggered Multiagent Optimization for Two-Layered Model of Hybrid Energy System With Price Bidding-Based Demand Response

Event-Triggered Multiagent Optimization for Two-Layered Model of Hybrid Energy System With Price Bidding-Based Demand Response

Event‐triggered mean square consensus of linear multiagent systems with measurement noises

Stochastic synchronization of delayed multiagent networks with intermittent communications: An impulsive framework

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