Design of proportional-integral reconfigurable control systems via eigenstructure assignment
Bibliographic record
Abstract
In this paper, an integrated design of fault detection, diagnosis and reconfigurable control for multi-input multi-output (MIMO) systems is proposed. The scheme uses proportional-integral (PI) control structure in the reconfigurable controller so as to recover both the dynamic and steady-state performance of the pre-fault system and to reject unknown constant disturbances. A singular value decomposition (SVD) based eigenstructure assignment (EA) technique is developed to achieve online automatic redesign of the controller. Fault diagnosis and controller reconfiguration mechanisms are carried out using statistical hypothesis tests based on the information from a two-stage adaptive Kalman filter. To achieve improved reconfiguration performance, a multiple reconfiguration controller design scheme is exploited. The proposed approach can deal with: abrupt and incipient faults; total and partial faults; single, multiple and consecutive faults with different type of reference inputs, and with the ability to detect and compensate for unanticipated actuator faults which are described by loss of control effectiveness. The effectiveness of the approach has been demonstrated via two simulation examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 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".