Robust Model Predictive Control for Asynchronously Switched Linear Systems With Intermittent Controller Failures
Bibliographic record
Abstract
Switched systems, as an exceptional modeling tool, may face threats to unexpected variations from the environment (e.g., external disturbances) or unreliable networks (e.g., desired controllers lagged to enabled subsystems or even controller disconnection). This article aims to study the robust model predictive control problem for a class of disturbed asynchronously switched systems with occasional controller disconnection. To mitigate the adverse effect of additive disturbances, a tube-based switched model predictive control strategy is designed by properly tightening original constraints. For the feasibility concern, the minimum mode-dependent dwell time is computed offline so as to ensure that reachable sets from an initial region are included in a common feasible set. Furthermore, a nonconservative stability condition is proposed for switched systems from a set-theoretic perspective. Based on this superior result, two stability strategies with distinct converging speeds are proposed to guarantee the closed-loop system to be uniformly asymptotically stable with constructed terminal constraints. Effectiveness of the theoretical results is validated via a simulation of time-varying communication networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".