Nonlinear Optimal Approach to Magnetic Spacecraft Attitude Control
Bibliographic record
Abstract
This paper proposes a nonlinear-based optimal magnetic actuation for spacecraft attitude control purposes. The proposed control design framework, which approximates the Hamilton-Jacobi-Bellman (HJB) equation, is essentially comprised of two steps; the value function involved in the HJB equation is first discretized by a finite truncated series of prescribed state-dependent basis functions and unknown coefficients with time-dependency. Galerkin’s spectral method is then applied directly to the HJB equation to determine time-dependent coefficients, thereby developing the desired nonlinear optimal control law. Employing this control architecture, the feedback controllers required to regulate the attitude motion of spacecraft are synthesized using the full nonlinear kinematics and dynamics of the system. This is particularly useful for attitude control systems which can involve large angle slewing maneuvers, thereby necessitating nonlinear controllers to appropriately compensate for the nonlinearities involved in the system. The simulation results show the feasibility of the proposed controller in terms of the magnetic torques required to correct the attitude of the spacecraft being considered in addition to its global asymptotic stability from a practical perspective.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".