Design and implementation of a novel sliding mode sensing architecture for capacitive MEMS accelerometers
Bibliographic record
Abstract
We propose novel feedback control and sensing schemes based on sliding mode control (SMC) for closed-loop micro-accelerometers as alternative digital control architectures to sigma–delta (ΣΔ) approaches. The under-damped micro-device has been designed in Coventorware, fabricated in SOIMUMPs (25 µm thick structural layer) technology and experimentally characterized using a Polytec MSA-500 (micro-system analyzer) equipment. To verify the system architecture robustness, the application of SMC is extended to an over-damped accelerometer model. In either case, the SMC demonstrates the repositioning of the proof mass to null position; however, the over-damped model exhibits shorter transition time (15 ms for 1g acceleration) due to the increased damping. In addition to that, we extend the usage of SMC beyond the classical actuation problem to a novel sensing problem where we demonstrate the extraction of the external acceleration measurement from the switching behavior along the sliding surface. An optimized fixed-point implementation is targeted on a field-programmable gate array (FPGA) using rapid prototyping methodology, where the new proposed method has been compared for reference with a control scheme that employs a ΣΔ modulator. The SMC-based architecture is advantageous in terms of hardware complexity, and the control of the number of degrees of freedom required by an inertial measurement unit can be accommodated on a low-cost FPGA device. SMC offers a sound theoretical framework for the nonlinear control of inertial sensors.
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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 | 0.001 | 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".