Modified Generalized Predictive Control of Networked Systems With Application to a Hydraulic Position Control System
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Bibliographic record
Abstract
This paper is concerned with the design of networked control systems using the modified generalized predictive control (M-GPC) method. Both sensor-to-controller (S-C) and controller-to-actuator (C-A) network-induced delays are modeled by two Markov chains. M-GPC uses the available output and prediction control information at the controller node to obtain the future control sequences. Different from the conventional generalized predictive control in which only the first element in control sequences is used, M-GPC employs the whole control sequences to compensate for the time delays in S-C and C-A links. The closed-loop system is further formulated as a special jump linear system. The sufficient and necessary condition to guarantee the stochastic stability is derived. Simulation studies and experimental tests for an experimental hydraulic position control system are presented to verify the effectiveness of the proposed method.
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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 | 0.002 | 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 | 0.000 |
| 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