Vibration Control of Satellite Antennas via NMPC and NARX Neural Networks
Bibliographic record
Abstract
This article introduces an integrated approach for vibration control in satellite plate antennas by combining advanced control techniques with smart materials. The proposed system integrates bonded piezoelectric actuators and sensors with two complementary control strategies: nonlinear model-predictive control (NMPC) and a Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network. A comprehensive coupled attitude and structural dynamics model is developed specifically for flexible plate-type antennas, providing the foundation for precise control implementation. The innovation lies in the synergistic combination of NMPC's predictive capabilities with NARX's adaptive learning features. While NMPC leverages the system model to optimize future control actions, the NARX neural network serves dual purposes: functioning as an independent controller and enhancing the system's state estimation accuracy. This hybrid approach addresses key limitations of traditional control methods, particularly in handling model uncertainties and external disturbances during spacecraft attitude maneuvers. MATLAB/Simulink simulations demonstrate that the integrated NMPC-NARX system significantly outperforms both stand-alone NMPC and NARX-based controllers in vibration suppression. The results show superior robustness to modeling inaccuracies and enhanced adaptability to dynamic disturbances, marking a significant advancement in satellite control system design. This research establishes a new paradigm for achieving improved stability and operational efficiency in satellite systems operating under uncertain conditions.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".