Observer-Based Event-Triggered Consensus Control of Multi-Agent Systems With Time-Varying Communication Delays
Bibliographic record
Abstract
This paper addresses a new observer-based asynchronous periodic event-triggered control approach for the consensus of linear multi-agent systems (MASs) to reduce the communication load in both sensor to observer (S-O) and controller to actuator (C-A) channels. In order to tackle the time-varying communication delays of the network, each agent uses a bank of observers to estimate the states of its own and its neighbour(s). First, the consensus of a MAS under periodic transmission and time-varying communication delay is analyzed, and the controller and observer gains are designed in terms of the feasibility of certain linear matrix inequalities (LMIs). Then, by using the emulation approach, the event-triggered consensus control problem is converted into the stability of a time-delay system, and by using Lyapunov-Krasovskii approach, a sufficient condition is derived such that the closed-loop system of all agents is stable and the event-triggering parameters are designed. We demonstrate that all agents’ states and observed states converge asymptotically to an agreement point. To illustrate the efficiency of the proposed strategy, a simulation study is performed, and the results show the efficiency of the observer-based ETC in terms of efficient use of communication resources and lower settling time. We have implemented the proposed method and conducted experiments on a real-world MAS consisting of a group of e-puck2 robots. The experimental results demonstrate the efficacy of the proposed method in achieving consensus while efficiently utilizing communication resources.Note to Practitioners—This paper delves into the event-triggered consensus control problem in networked multi-agent systems (MASs), considering time-varying communication delays. The proposed approach holds significant promise for applications in MASs and distributed systems, such as satellite-based communication systems, vehicular networks, swarm robotics, and smart grid control. Since we have considered time-varying delays in both sensor-to-observer and controller-to-actuator channels, this method is applicable in scenarios where the controller and actuator of each agent are physically separated, and sensor and control signals are being transmitted over a network. Moreover, we have significantly reduced the amount of data transmitted over the network through the use of event-triggered control, which is beneficial in applications with limited bandwidth, congested networks, and limited energy supplies. To validate the theoretical results with experimental tests, the proposed method is applied to the a group of robots in the consensus control problem. The same approach can be extended to MASs under more realistic network-induced delays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".