Robust Output Feedback MPC for Networked Control Systems with Two-Channel Random Packet Dropouts
Bibliographic record
Abstract
In this paper, we focus on the robust output feedback Model Predictive Control (MPC) design for linear constrained Networked Control Systems (NCSs) subject to disturbances, observation noise and random packet dropouts in both Sensor-Controller (S-C) and Controller-Actuator (C-A) channels. The proposed control scheme consists of an observer to estimate the state and a robust model predictive controller to stabilize the disturbed system. In the observer design, we extend the Luenberger observer to estimate the state in two communication scenarios. The resulting dynamics of estimation error can be described by a switched system. With this, a Generalized Robust Positive Invariant (GRPI) set can be developed, providing an explicit bound of estimation errors in the presence of admissible disturbances and packet dropouts. Similarly, a GRPI set is established to bound the prediction error in the MPC framework under the proposed state estimator. These two GRPI sets are further used to develop tightened constraints in the proposed robust output feedback MPC scheme to ensure robust constraint satisfaction. It is rigorously proved that the proposed robust MPC algorithm is recursively feasible and the system state converges to a compact set around the origin. Finally, simulation results are provided to verify the effectiveness of the proposed robust output feedback MPC scheme.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".