Event-triggered consensus of multi-agent systems with noises

Hu, Aihua; Cao, Jinde; Hu, Manfeng; Guo, Liuxiao

doi:10.1016/j.jfranklin.2014.08.005

Cited by 62 publications

(39 citation statements)

References 35 publications

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“…Now, we are in the position to analyze inequality (44). The right side of (44) can be partitioned as three items.…”

Section: Theorem 14 (Optimization) Let the Weight-balanced Assumptiomentioning

confidence: 99%

“…Meanwhile, the distributed convex optimization problems over general networks were settled in Lobel et al [38] and Matei and Baras [39]. Recent works [40][41][42][43][44][45] have coordinately put their efforts on the consensus of multiagent systems by designing the control protocols based on event-triggered sampling schemes. Lu and Tang [41] proposed a continuous-time consensus-based zerogradient-sum (ZGS) algorithm which is built on the condition of the gradient sum being zero.…”

Section: Introductionmentioning

confidence: 99%

“…Seyboth et al [42] studied a variant of the event-trigger average consensus problem for single-integrators and double-integrators, where a novel control strategy for multiagent coordination was employed to simplify the performance and convergence analysis of the method. To solve the optimization problem with more general case that the mean square consensus for multiple agents is affected by noises over directed networks, Hu et al [44] proposed a novel centralized and decentralized eventtriggered protocols and built its convergence. In more recent literature, Li et al [45] investigated event-triggered nonlinear consensus in directed multiagent systems with combinational state measurements.…”

Section: Introductionmentioning

confidence: 99%

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Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

Lü

2017

Complexity

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This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent's own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every agent updates its state by applying its own objective function and the information sent from its neighboring agents. Under the assumption that the network topology is uniformly strongly connected and weight-balanced, the novel event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converging to an optimal solution of the convex optimization problem. Finally, a simulation example is given to validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis.

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“…Now, we are in the position to analyze inequality (44). The right side of (44) can be partitioned as three items.…”

Section: Theorem 14 (Optimization) Let the Weight-balanced Assumptiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

Lü

2017

Complexity

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“…The 1st or 2nd order systems [17] General systems [7] Triggering conditions Absolute change conditions [13] Relative change conditions [11,17] Performances Asymptotic consensus [7,11,17] Bounded consensus [13,19] Design parameters…”

mentioning

confidence: 99%

“…Controller parameters [7,11,17] Controller & trigger parameters [13,19] Compared with the time-triggered control scheme [2,3,15,16], the distinct feature of event-triggered control strategy is that the control input is updated only when certain specific event happens. Between updates of the controller inputs, the control signal remains unchanged, and an appropriately generated updating time would guarantee the stability and performance of the closed-loop system.…”

mentioning

confidence: 99%

Event-triggered consensus control for discrete-time stochastic multi-agent systems: The input-to-state stability in probability

et al. 2015

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This paper is concerned with the event-triggered consensus control problem for a class of discrete-time stochastic multi-agent systems with state-dependent noises. A novel definition of consensus in probability is proposed to better describe the dynamics of the consensus process of the addressed stochastic multi-agent systems. The measurement output available for the controller is not only from the individual agent but also from its neighboring ones according to the given topology. An event-triggered mechanism is adopted with hope to reduce the communication burden, where the control input on each agent is updated only when a certain triggering condition is violated. The purpose of the problem under consideration is to design both the output feedback controller and the threshold of the triggering condition such that the closed-loop system achieves the desired consensus in probability. First of all, a theoretical framework is established for analyzing the so-called input-to-state stability in probability (ISSiP) for general discrete-time nonlinear stochastic systems. Within such a theoretical framework, some sufficient conditions on event-triggered control protocol are derived under which the consensus in probability is reached. Furthermore, both the controller parameter and the triggering threshold are obtained in terms of the solution to certain matrix inequalities involving the topology information and the desired consensus probability. Finally, a simulation example is utilized to illustrate the usefulness of the proposed control protocol.

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An Overview of Cooperative and Consensus Control of Multiagent Systems

Davoodi

Gallehdari

Saboori

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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There has been a growing interest toward the development of networked unmanned autonomous systems that can operate without an extensive involvement of humans. The motivation for this focus can be traced to the emergence of applications where direct human intervention is not possible due to the environmental hazards, complexity of the tasks, or other restrictions. These networks can consist of a large number of dynamical systems (agents), such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs). These systems commonly include a number of sensors, actuators, and decisionmakers. Therefore, the network of these systems is a network of a large number of sensors and actuators or, as is known in the literature, a system of systems (SoS). In this work, we provide a brief overview of controlling these networks, their applications, and the solutions that are proposed for these problems in the literature. Specifically, this article overviews recent results and progress made on multiagent consensus by focusing on cooperative control and consensus (formation) control in the presence of communication, control implementation, and saturation constraints. In addition, we review the active areas of event‐triggered consensus control, network security and attacks on the agents, and networks and resilient consensus control strategies that are proposed to handle these challenges.

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Event-triggered consensus of multi-agent systems with noises

Cited by 62 publications

References 35 publications

Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

Event-Triggered Discrete-Time Distributed Consensus Optimization over Time-Varying Graphs

Event-triggered consensus control for discrete-time stochastic multi-agent systems: The input-to-state stability in probability

An Overview of Cooperative and Consensus Control of Multiagent Systems

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