Robust Output Feedback MPC for Networked Control Systems with Two-Channel Random Packet Dropouts
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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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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