Scheduling Local Information Exchange in Linear Multiagent Systems Through an Event-Triggering Approach

2022 ◽  
Author(s):  
Stefan Ristevski ◽  
Tansel Yucelen ◽  
Jonathan A. Muse
Keyword(s):  
Multiagent Systems ◽  
Information Exchange ◽  
Local Information ◽  
Event Triggering

A Distributed Mechanism for Probing Overlay Path Bandwidth Using Local Information Exchange

2014 ◽  
Vol E97.B (5) ◽  
pp. 981-995
Author(s):  
Tien Hoang DINH ◽  
Go HASEGAWA ◽  
Masayuki MURATA
Keyword(s):  
Information Exchange ◽  
Local Information

scholarly journals Bipartite Consensus of Heterogeneous Multiagent Systems Based on Distributed Event-Triggered Control

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoyu Wang ◽  
Kaien Liu ◽  
Zhijian Ji ◽  
Shitao Han
Keyword(s):  
Multiagent Systems ◽  
Sufficient Conditions ◽  
Input Delay ◽  
Consensus Problem ◽  
Consensus Protocol ◽  
Control Scheme ◽  
Event Triggering ◽  
Bipartite Consensus ◽  
Theoretical Results ◽  
Event Triggered

In this paper, the bipartite consensus problem of heterogeneous multiagent systems composed of first-order and second-order agents is considered by utilizing the event-triggered control scheme. Under structurally balanced directed topology, event-triggered bipartite consensus protocol is put forward, and event-triggering functions consisting of measurement error and threshold are designed. To exclude Zeno behavior, an exponential function is introduced in the threshold. The bipartite consensus problem is transformed into the corresponding stability problem by means of gauge transformation and model transformation. By virtue of Lyapunov method, sufficient conditions for systems without input delay are obtained to guarantee bipartite consensus. Furthermore, for the case with input delay, sufficient conditions which include an admissible upper bound of the delay are obtained to guarantee bipartite consensus. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.

A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications

2017 ◽  
Vol 8 (3) ◽  
pp. 15-36 ◽  
Author(s):  
Jing Wang ◽  
In Soo Ahn ◽  
Yufeng Lu ◽  
Tianyu Yang ◽  
Gennady Staskevich
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Multiagent Systems ◽  
Information Exchange ◽  
Large Scale ◽  
Wireless Sensor ◽  
Left Eigenvector ◽  
Mild Assumption ◽  
Least Squares Algorithm

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.

scholarly journals Distributed Event-Triggered Control of Multiagent Systems with Time-Varying Topology

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jingwei Ma ◽  
Jie Zhou ◽  
Yanping Gao
Keyword(s):  
Multiagent Systems ◽  
Linear Matrix Inequalities ◽  
Linear Matrix ◽  
Control Input ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Common Lyapunov Function ◽  
Event Triggering ◽  
Lyapunov Function Method ◽  
Event Triggered

This paper studies the consensus of first-order discrete-time multiagent systems, where the interaction topology is time-varying. The event-triggered control is used to update the control input of each agent, and the event-triggering condition is designed based on the combination of the relative states of each agent to its neighbors. By applying the common Lyapunov function method, a sufficient condition for consensus, which is expressed as a group of linear matrix inequalities, is obtained and the feasibility of these linear matrix inequalities is further analyzed. Simulation examples are provided to explain the effectiveness of the theoretical results.

scholarly journals Leader-Follower Fixed-Time Group Consensus Control of Multiagent Systems under Directed Topology

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yilun Shang ◽  
Yamei Ye
Keyword(s):  
Distributed Control ◽  
Multiagent Systems ◽  
Initial Conditions ◽  
Fixed Time ◽  
Local Information ◽  
Convergence Time ◽  
Group Consensus ◽  
Consensus Control ◽  
Directed Topology ◽  
Theoretical Results

This paper investigates the fixed-time group consensus problem for a leader-follower network of integrators with directed topology. A nonlinear distributed control protocol, based on local information, is proposed such that the follower agents in each subgroup are able to track their corresponding leaders in a prescribed convergence time regardless of the initial conditions. Simulation examples are presented to demonstrate the availability of our theoretical results.

Event-triggered particle filtering and Cramér–Rao lower bound computation

2020 ◽  
pp. 095965182094932
Author(s):  
Nargess Sadeghzadeh-Nokhodberiz ◽  
Mohammadreza Davoodi ◽  
Nader Meskin
Keyword(s):  
Nonlinear Systems ◽  
Lower Bound ◽  
Particle Filtering ◽  
Information Exchange ◽  
Minimum Mean Square Error ◽  
Stochastic Nonlinear Systems ◽  
Event Triggering ◽  
Estimation Scheme ◽  
Cramer Rao Lower Bound ◽  
Event Triggered

In this article, an event-triggered particle filtering method is presented to estimate the states of stochastic nonlinear systems with the ultimate goal to reduce the information exchange in networked systems. In the event-triggered estimation, measurements are transferred to an estimator only if certain event conditions are satisfied. Using these event-triggered measurements leads to non-Gaussianity of the conditional posterior distribution in minimum mean square error estimators even in the presence of Gaussian process and measurement noises. Therefore, in this article, a particle filter–based method is employed to solve the non-Gaussianity issue in nonlinear systems due to event-triggered measurements. In the proposed scheme, when no information is sent to the estimator, particles weight update role is modified according to the event-triggering probability density function. To evaluate the performance of the proposed state estimation scheme, the conditional posterior Cramér–Rao lower bound is obtained using Monte Carlo simulations. The bound is also computed for nonlinear Gaussian systems with a Gaussian event-triggering mechanism as a special case. Finally, the efficiency of the proposed method is demonstrated for a networked interconnected four-tank system through simulation and a comparison study is also provided.

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