Robust Output Feedback Model Predictive Control for Networked Control Systems Subject to Random Packet Dropouts
Bibliographic record
Abstract
This paper proposes a robust output feedback model predictive control (MPC) framework for constrained linear networked control systems (NCSs) subject to random packet dropouts and external disturbances. The proposed output feedback MPC scheme consists of a state observer that accommodates the random measurement loss and a state feedback model predictive controller that stabilizes the perturbed system. According to the proposed observer, the estimation error dynamics can be represented by a switched system. By developing a generalized robust positive invariant (GRPI) set under the switched system formulation, the estimation error can be confined in this invariant set, which serves as the explicit error bound of state estimation. Then, the GRPI set is utilized to tighten the state and input constraints in the MPC optimization problem to alleviate the effects of random packet dropouts and disturbances. As a result, the system can be stabilized by the proposed output feedback model predictive controller while both state and input constraints are fulfilled. Simulation results are provided to validate the effectiveness of the proposed method.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".