Cloud-Supported Formation Control of Second-Order Multiagent Systems

Adaldo, Antonio; Liuzza, Davide; Dimarogonas, Dimos V.; Johansson, Karl Henrik

doi:10.1109/tcns.2017.2732819

Cited by 30 publications

(18 citation statements)

References 20 publications

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“…Early works generally assumed that agents have continuous, or at least periodic, access to information about their neighbors. Consequently, several groups of researchers have considered applying the event-triggered coordination ideas to consensus-based formation control algorithms to relax this requirement (Chu et al, 2018;Adaldo et al, 2017).…”

Section: Applications Of Event-triggered Consensusmentioning

confidence: 99%

Event-triggered communication and control of networked systems for multi-agent consensus

2019

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This article provides an introduction to event-triggered coordination for multi-agent average consensus. We provide a comprehensive account of the motivations behind the use of event-triggered strategies for consensus, the methods for algorithm synthesis, the technical challenges involved in establishing desirable properties of the resulting implementations, and their applications in distributed control. We pay special attention to the assumptions on the capabilities of the network agents and the resulting features of the algorithm execution, including the interconnection topology, the evaluation of triggers, and the role of imperfect information. The issues raised in our discussion transcend the specific consensus problem and are indeed characteristic of cooperative algorithms for networked systems that solve other coordination tasks. As our discussion progresses, we make these connections clear, highlighting general challenges and tools to address them widespread in the event-triggered control of networked systems.

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Section: Applications Of Event-triggered Consensusmentioning

confidence: 99%

Event-triggered communication and control of networked systems for multi-agent consensus

2019

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“…Sliding model control, as an effective robust control strategy has also been applied to NCSs [76,196]. With the development of cloud computing technologies, the cloud-based networked control has exhibited potential advantages [4,229].…”

Section: General and Augmented Lyapunov Functional Methodsmentioning

confidence: 99%

Survey on time-delay approach to networked control

Liu

Selivanov

Fridman

2019

Annual Reviews in Control

133

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This paper provides a survey on time-delay approach to networked control systems (NCSs). The survey begins from a brief summary on fundamental network-induced issues in NCSs and the main approaches to the modelling of NCSs. In particular, a comprehensive introduction to time-delay approach to sampled-data and networked control is provided. Then, recent results on time-delay approach to event-triggered control are recalled. The survey highlights time-delay approach developed to modelling, analysis and synthesis of NCSs, under communication constraints, with a particular focus on Round-Robin, Try-once-discard and stochastic protocols. The time-delay approach allows communication delays to be larger than the sampling intervals in the presence of scheduling protocols. Moreover, some results on networked control of distributed parameter systems are surveyed. Finally, conclusions and some future research directions are briefly addressed.

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“…Recently, event-and self-triggered sampling [21][22][23][24][25][26][27][28][29][30][31][32][33] have been introduced into multi-agent coordinated control [32,[34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], and its sampling times are determined by event-triggered conditions rather than by synchronous or asynchronous digital clock signals. In comparison with traditional periodic sampled-data control, distributed event-triggered control performs data sampling according to the needs of agents, avoiding redundant sampling at unnecessary times, thereby saving system resources.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with traditional periodic sampled-data control, distributed event-triggered control performs data sampling according to the needs of agents, avoiding redundant sampling at unnecessary times, thereby saving system resources. According to the information acquisition methods of the sensors equipped by agents, distributed event-triggered sampling can usually be divided into node-based [34][35][36]38,44,52,53] and edge-based [39][40][41][42][43][45][46][47]54,55]. These two methods are suitable for different scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in the deep sea or deep space, each AUV or spacecraft employs sonar or LiDAR to measure the relative velocity and position between itself and its neighbors to achieve multi-agent coordination goals. In [39,[45][46][47]54,55], all relevant edge state information of each agent is acquired and processed together, which is different from the way of asynchronously acquiring and processing edge state information [40][41][42][43]45]. However, most of the previous results have not considered the controller output nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Edge-Event-Triggered Synchronization for Multi-Agent Systems with Nonlinear Controller Outputs

et al. 2020

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This paper addresses the synchronization problem of multi-agent systems with nonlinear controller outputs via event-triggered control, in which the combined edge state information is utilized, and all controller outputs are nonlinear to describe their inherent nonlinear characteristics and the effects of data transmission in digital communication networks. First, an edge-event-triggered policy is proposed to implement intermittent controller updates without Zeno behavior. Then, an edge-self-triggered solution is further investigated to achieve discontinuous monitoring of sensors. Compared with the previous event-triggered mechanisms, our policy design considers the controller output nonlinearities. Furthermore, the system’s inherent nonlinear characteristics and networked data transmission effects are combined in a unified framework. Numerical simulations demonstrate the effectiveness of our theoretical results.

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Cloud-Supported Formation Control of Second-Order Multiagent Systems

Cited by 30 publications

References 20 publications

Event-triggered communication and control of networked systems for multi-agent consensus

Event-triggered communication and control of networked systems for multi-agent consensus

Survey on time-delay approach to networked control

Edge-Event-Triggered Synchronization for Multi-Agent Systems with Nonlinear Controller Outputs

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