Sliding Mode for an All-Digital Control and Readout of MEMS Gyroscopes
Bibliographic record
Abstract
Mobile devices and sensor networks advances have sharply increased the demand for inertial MEMS sensors with a direct digital output. This paper reports on the first implementation of a MEMS-based gyroscope system with all-digital control and readout, using sliding mode and bitstream processing for both the driving and sensing modes. The in-plane vibrating MEMS gyroscope, sensitive to out-of-plane external angular rates, was designed in a custom 50um SOI technology. A band-pass sliding mode control (SMC), equivalent to a digital PLL control, was used for electrically driving one of the resonant modes at its resonant frequency, and track its drift (due to environment variations). Simulations indicate a fast capture time in response to step variations (high tracking speed of 11,486 rad/s2) and small continuous tracking errors, below 0.0083 %. A regular (low-pass) sliding mode control was used for the sensing mode, in order to cancel its vibration and thus improve its linearity. The feedback loop action was proven to attenuate the displacement in the sensing mode by more than 100 times. The resulting bitstream output (that generates in the SMC loop the high-frequency cancelling electrostatic forces) contains the relevant information about the Coriolis force, and a bitstream synchronous demodulation technique is used to reconstruct the input angular rate to be measured, with a reconstruction error of ±0.2 rad/s. The overall system enables an easy future implementation on a MEMS+FPGA of a high-performance angular rate sensing microsystem with digital output.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".