Fault-tolerant control design for unreliable networked control systems via constrained model predictive control
Bibliographic record
Abstract
This work deals with the problem of passive fault-tolerant control (FTC) for discrete-time networked control systems (NCSs). Network imperfections such as random time delay and packet dropout are modeled as a Markov chain that results in a Markovian jump linear system (MJLS). Some of the elements in the transition probability matrix (TPM) are supposed to be unknown so as to address network complexities. In addition, a comprehensive and practical fault model that considers the stochastic nature of networks is employed. By utilizing this fault model, the closed-loop NCS model is obtained by means of state augmentation technique. Then, a constrained model predictive control (MPC) is proposed to develop a fault-tolerant control strategy in which all these issues are considered as well as input constraint. Sufficient conditions to design the proposed reliable controller are derived in terms of linear matrix inequalities (LMIs). Finally, two examples are utilized to demonstrate the validity of the proposed FTC. The simulation results show that the proposed strategy works well, and results in more effective responses compared to state-of-the-arts studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".