QUEST-Based Kalman Filter and LQR for Satellite Attitude Control
Bibliographic record
Abstract
This paper presents the design and implementation of accurate attitude estimation and control using low-cost Inertial Measurement Unit (IMU) sensors based on a satellite quaternion model. Mathematical models of the proposed algorithms are outlined and discussed extensively. A two-step optimal estimator from vector observations is proposed. QUEST algorithm is used to produce simultaneous non-collinear vector measurements of the sensors which are then fed into the Kalman filter to obtain better attitude estimates. Linear-Quadratic Regulator control is implemented using a reduced quaternion satellite model. Simulation results show that the two-step filter approach performs satisfactorily in eliminating the bias and error that is present in IMU sensor measurements. The optimal attitude is used to simulate quaternion-based attitude and angular velocity responses to LQR. The results show that the control strategy performs satisfactorily in making sure that there is a minor difference between the reference input attitude and estimated attitude (from the Kalman filter).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".