Controller Tuning and Robustness Testing of Attitude Control Laws for a CubeSat Mission
Bibliographic record
Abstract
The purpose of this project was to create a test environment that can be used to test different controllers and their robustness. In this report, the equations of motion were derived using kinematics, with attitude quaternions, and spacecraft dynamics, with angular velocity and acceleration. The equations were combined and placed into the form of a linearized state-space equation. The different control methods being investigated, Linear Quadratic Regulator (LQR) for the reaction wheel model, and the Bdot with bias controller, were explained and the block diagram for each was shown. To setup the test, the tolerances for the roll, pitch, and yaw, and their rates, were taken from the mission requirement for the ESSENCE mission. The attitude tolerance being ±0.5deg and the angular rates requirement being ±0.05deg/s. Then the test setup was further explained. The test is broken up into different scripts and steps: 1. Main run function for simulation. Initializes simulation parameters. 2. Build state-space equation and calculate constant gain matrix. 3. Randomize initial conditions and pass onto simulation. 4. Post-processing and plot generation. 5. Statistics generation. This robust testing environment was used to test 5 different controllers for the reaction wheel model. Each controller was tested for 200 different simulations, in which the initial attitude, initial angular rates, and the center of mass were randomized. The first controller was successful for 198/200 simulations, where the only failure came from over-saturating the reaction wheels. The next three controllers had a perfect record and were successful for all 200 simulations each. The last controller, had only 71 successful simulations in the set, and a sample of one of the failed simulations was further investigated to see how it failed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".