Related Experiment Video
Updated: Nov 27, 2025
06:48
The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
9.7K
On Leader-Following Consensus in Multi-Agent Systems with Discrete Updates at Random Times
Ricardo Almeida1, Ewa Girejko2, Snezhana Hristova3
1Center for Research and Development in Mathematics and Applications, Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal.
Entropy (Basel, Switzerland)
|December 8, 2020
Summary
This study addresses leader-following consensus in multi-agent systems with random communication updates. It provides control design conditions to achieve consensus reliably, confirmed by numerical examples.
Area of Science:
- Control Systems Engineering
- Networked Systems
- Stochastic Processes
Background:
- Leader-following consensus is crucial for coordinated multi-agent systems.
- Real-world systems often experience communication delays or intermittent updates.
- Stochastic variations in communication timing pose significant challenges.
Purpose of the Study:
- To investigate leader-following consensus in continuous-time multi-agent networks.
- To analyze systems where controller updates occur at exponentially distributed random times.
- To develop a control law ensuring asymptotic consensus under these stochastic conditions.
Main Methods:
- Derivation of sufficient conditions for consensus.
- Design of a control law considering exponentially distributed update times.
- Analysis using stochastic process expected values.
Main Results:
- Sufficient conditions for achieving asymptotic leader-following consensus were established.
- The proposed control law guarantees consensus despite random communication updates.
- Numerical simulations validated the theoretical findings.
Conclusions:
- The derived conditions and control law are effective for leader-following consensus in stochastic networks.
- This research contributes to robust control design for intermittently communicating multi-agent systems.
- The findings are applicable to scenarios requiring reliable coordination with unpredictable communication.
Related Concept Videos
01:13Randomized Experiments
8.6K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
8.6K
01:23Actor-Observer Effect
143
The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
143
01:09Random Sampling Method
13.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
13.8K
00:59Propagation of Uncertainty from Random Error
1.5K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.5K
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
181
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
181
01:24BIBO stability of continuous and discrete -time systems
764
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
764