Development of novel satellite attitude determination and control algorithms based on telemetry data from an Earth satellite
Bibliographic record
Abstract
All spacecraft missions require accurate knowledge of attitude, which is derived from on-board sensors using attitude determination algorithms. The increasing demands for attitude accuracy, high performance and low cost spacecraft are driving designers to change from available attitude determination methods to those that are more robust and accurate. However, the cost, the processor workload and the time-constraints in spacecraft development and deployment projects curtail the opportunity for developing new on-board attitude determination methods, especially with regards to the development of more precise sensors. Therefore, it is always desired to achieve the required attitude accuracy with the existing set of on-board sensors, but using effective attitude determination methods and sensor fusion algorithms. Developing such algorithms starts on the ground and is subject to verification and tuning with real experimental data from telemetry. Moreover, the on-ground mission control center has to evaluate the attitude accuracy, calibrate sensors and performance. Motivated by these needs, the main objective of this thesis is to develop novel attitude determination algorithms combining several sensors and attitude estimation methods for Ground-Based Attitude Estimation (GBAE) with telemetry data. The GBAE formulation will be based on a guaranteed ellipsoidal state estimation for acquisition mode and a modified Kalman filter for pointing mode, to provide optimal attitude estimates of the spacecraft. The GBAE has to be evaluated both in the simulation environment and in the flight environment. In the simulation environment, the evaluation of the GBAE rests on the availability of an accurate dynamical model for the spacecraft. However, spacecraft dynamics are complex with multiple modes of operation. Moreover, the nonlinearities in the actual system make the spacecraft dynamics more complex. This motivates the use of switching between a global nonlinear controller for acquisition mode and a local linear controller for pointing mode, which can guarantee performance and is less computationally intensive for implementation in an on-board microprocessor. In this thesis, novel attitude determination and control algorithms are evaluated in the flight environment for a case study in collaboration with the Canadian Space Agency for the SCISAT-1 satellite.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".