An Immune System-Inspired Reconfigurable Controller
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on the biological immune mechanism, a design approach for the immune reconfigurable controller (IRC) is proposed. Using four units to imitate the immune system's surveillance process, response process, memory mechanism, and self-learning process, respectively, the IRC is capable of actuator fault detection and fault-tolerant control for multi-input multioutput systems. Meanwhile, in order to further improve the control performance, an online optimization process with the multiobjective clonal selection algorithm is designed. To verify its effectiveness, the IRC is applied to the coagulation bath of polyacrylonitrile carbon fiber production line. Comparison experiments with conventional PID and reconfigurable model-based predictive controller control schemes are conducted. The simulation results demonstrate that the IRC can rapidly eliminate the fluctuation due to the actuator fault and guarantees the stability of the coagulation bath. In addition, the IRC has the ability of quick response to the same failure as well as unknown faults.
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.
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 | 0.001 | 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