Active Disturbance Rejection Control and Parameter Tuning Optimization for Micronewton Fluidic System
Bibliographic record
Abstract
A control strategy based on the combination of Linear Active Disturbance Rejection Control (LADRC) and Particle Swarm Optimization (PSO) algorithm is proposed to address the problem of precise control of microNewton cold gas thruster, providing effective technical support for their applications in space gravitational wave detection and high-precision measurement of the Earth's gravity field. The mathematical models of the piezoelectric drive system and Laval nozzle are obtained by parameter identification of the system through the PSO algorithm. Compared with the traditional PID control, the Linear Active Disturbance Rejection Control (LADRC) exhibits stronger anti-disturbance capability and faster response speed. Simulation results show that the LADRC controller exhibits obvious advantages under different operating conditions: the rise time is shortened by about 20%, the regulation time is reduced by about 25%, the overshoot is significantly reduced, and the steady-state error is close to zero.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".