Event-Triggered Tracking Control With Filtered Outputs and Impulsive Observers
Bibliographic record
Abstract
This article studies an event-triggered tracking control problem for linear systems subject to output feedback and disturbances, where a new configuration incorporating filtered outputs and impulsive observers is proposed. The transmissions in the sensor-to-controller and controller-to-actuator channels are scheduled by dynamic event-triggered control (ETC) mechanisms to save communication resources. To eliminate the effects of the derivatives of output noises on the tracking and transmission performance, a low-pass filter is introduced to preprocess the raw output signals. Both the filter state and raw output will be transmitted to the controller node while the latter is only utilized by an impulsive observer at some discrete instants. Then, it is proved that the proposed dynamic ETC schemes can solve the practical tracking control problem with fixed reference points and avoid Zeno behavior in both channels. Meanwhile, when some user-specified parameters in the event-triggering conditions are small enough, the tracking control problem can be solved asymptotically for disturbance-free systems. In addition, to further improve the transient performance, reduced-order impulsive observers and optimization of impulsive gain matrices are studied. Finally, simulation results are provided to illustrate the efficiency and feasibility of the obtained results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".