Cross-axis coupling suppression method based on TITO-SGPC for active magnetic compensation system
Bibliographic record
Abstract
Abstract This study addresses the challenge of cross-axis magnetic field coupling in active magnetic compensation (AMC) system for magnetic shielding room (MSR), which limits control accuracy in weak magnetic environment. A three-input three-output (TITO) model of AMC system is constructed, and a simplified generalized predictive control (TITO-SGPC) is proposed, using attenuation sequence to reduce high-dimensional matrix operations. Experimental results show that TITO-SGPC suppresses cross-axis coupling magnetic field within 0.34 nT, achieving over 2.4 times higher suppression than traditional GPC. Compared with multiple-input multiple-output model predictive control (MIMO-MPC), the computational complexity is significantly reduced, the execution time and memory usage are reduced by 14 times and 2.2 times, respectively, and it has better disturbance suppression effect. This method overcomes the limitations of traditional decentralized control and computationally intensive MIMO methods by explicitly establishing a three-axis cross model, and uniquely balances coupling suppression and real-time performance. This study provides a modular and high-precision solution for biomagnetic measurement, enabling stable control in MSRs with different geometries and supporting advancements in weak magnetic measurement technology.
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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 | 0.000 | 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) | 0.001 | 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".