Event-Based Finite-Time Consensus Control of Second-Order Delayed Multi-Agent Systems

Ran, Guangtao; Liu, Jian; Li, Chuanjiang; Chen, Liangming; Li, Dongyu

doi:10.1109/tcsii.2020.2999480

Cited by 42 publications

(27 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Numerous studies have attempted to eliminate the need for continuous monitoring in ETC. For instance, continuous communication and selfstate monitoring are avoided in [65]. In [66], mixed time-and event-triggered observers are presented to estimate the state of a system with discontinuous monitoring.…”

Section: Preliminaries a Event-triggered And Self-triggered Controlmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

View full text Add to dashboard Cite

Network control systems have gained considerable attention over the last decade as a result of the trend towards decentralised control applications and the emergence of cyber-physical system applications. However, real-world network control systems suffer from limited communication bandwidths, reliability issues, and a lack of awareness of network dynamics due to the complex nature of wireless networks. Combining machine learning and event-triggered control has the potential to alleviate some of these issues. For example, machine learning can be used to overcome the problem of a lack of network models by learning system behavior or adapting to dynamically changing models by continuously learning model dynamics. Event-triggered control can help to conserve communication bandwidth by transmitting control information only when necessary or when resources are available. The purpose of this article is to conduct a review of the literature on the use of machine learning in combination with event-triggered control. Machine learning techniques such as statistical learning, neural networks, and reinforcement learning-based approaches such as deep reinforcement learning are being investigated in combination with event-triggered control. We discuss how these learning algorithms can be used for different applications depending on the purpose of the machine learning use. Following the review and discussion of the literature, we highlight open research questions and challenges associated with machine learning-based event-triggered control and suggest potential solutions.

show abstract

Section: Preliminaries a Event-triggered And Self-triggered Controlmentioning

confidence: 99%

“…For example, ETC is applied to asynchronous control of cyber-attacks in [73]. Parameter uncertainties, actuator non-linearity, and sensor saturation are also considered under ETC in [65].…”

Section: Preliminaries a Event-triggered And Self-triggered Controlmentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The task of transporting the hazardous material is completed by all robots cooperatively. 3 A basic question in containment control is to design containment algorithms, and many algorithms have been developed to address containment problems including communication topologies, [4][5][6][7] communication delays 3,[8][9][10] and agent dynamics (e.g., first-order systems, [1][2][3]6,[9][10][11] second-order systems, 7,12-14 higher-order systems, 4,5,15 and uncertain system 16,17 ). Recently, some novel control strategies have been put forward for switched MASs 18 or heterogeneous MASs.…”

Section: Introductionmentioning

confidence: 99%

Containment problem for multi‐agent systems with position and velocity constraints

Chen

Wang

Pan

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

The containment problem is investigated for networked second‐order agents with nonuniform position and nonconvex velocity constraints under directed switching topologies. A distributed containment algorithm is designed for each agent to drive it into the convex target area formed by multiple leaders. The proposed control strategy only utilizes the information of each agent and its neighbor interaction. Theoretical analysis demonstrates that the constrained containment problem can be addressed only if each agent has a directed communication path from at least one leader to it in the union of the graphs. Finally, several numerical simulation results are presented to demonstrate the proposed control schemes.

show abstract

“…Besides, how to improve the convergence rate should be considered. In Hu et al ( 2019 ), Zou et al ( 2019 ), Dong et al ( 2020 ), and Ran et al ( 2020 ), the distributed control protocols and their convergence analyses have been investigated for finite-time consensus of multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed Averaging Problems of Agriculture Picking Multi-Robot Systems via Sampled Control

Yao

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

Distributed control of agriculture picking multi-robot systems has been widely used in the field of smart agriculture, this paper aims to explore the distributed averaging problems of agriculture picking multi-robot systems under directed communication topologies by taking advantage of the sampled data. With the algebraic graph theory concepts and the matrix theory, a distributed protocol is proposed based on the nearest sampled neighbor information. It is shown that under the proposed protocol, the states of all agents can be guaranteed to reach average consensus whose value is the averaging of the initial states of all agents. Besides, when considering time-delay, the other distributed protocol is constructed, in which a time margin of the time-delay can be determined simultaneously. The necessary and sufficient consensus results can be developed even though the time delay exists. Simulation results are given to demonstrate the effectiveness of our developed consensus results.

show abstract

Event-Based Finite-Time Consensus Control of Second-Order Delayed Multi-Agent Systems

Cited by 42 publications

References 35 publications

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Containment problem for multi‐agent systems with position and velocity constraints

Distributed Averaging Problems of Agriculture Picking Multi-Robot Systems via Sampled Control

Contact Info

Product

Resources

About