Network-Based Predictive Control for Constrained Nonlinear Systems With Two-Channel Packet Dropouts
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Bibliographic record
Abstract
This paper investigates the predictive control scheme and the associated stability issue for the constrained nonlinear networked control systems (NCSs), where both the sensor-to-controller packet dropout and the controller-to-actuator packet dropout are considered simultaneously. The model predictive control based framework is proposed to compensate for the two-channel packet dropouts. This framework consists of two main aspects: 1) to design the control packets by solving a constrained optimization problem and 2) to synthesize an efficient packet transmission and compensation mechanism based on the Transmission Control Protocol. To study the stability of the resultant nonlinear NCS, we propose a novel Lyapunov function, based on which the conditions for ensuring the regional input-to-state practical stability are developed. Finally, the proposed control strategy is applied to a spring-and-cart system to demonstrate the applicability and effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it