Kalman-filter-based Accurate Trajectory Tracking and Fault-Tolerant Controlof Quadrotor
Bibliographic record
Abstract
A Kalman filter(KF)-based feedforward-feedback controller is proposed using the internal model(IM)-principle for accurate tracking of a desired trajectory, and fault-tolerant control of a quadrotor, despite input and output sensor measurements being affected by unknown disturbances, measurement noise and model perturbations.The quadrotor model is unstable and nonlinear.Its input is a nonlinear function of the roll, pitch and yaw, and its output is its position in the ground-fixed coordinates.The quadrotor is subject to model uncertainties, disturbances including wind gusts, aerodynamic drags, gravitational load, and Coriolis forces, and the inputs and the outputs are affected by unknown stochastic disturbances and measurement noise.Predictive analytics is used to estimate the true input by exploiting its smoothness and the randomness of the noisy input.The nonlinear system is better approximated using the linear parameter-varying (LPV) model described by piecewise-linear Box-Jenkins model at each operating point, than by conventional approximation techniques.The system and the associated Kalman filter (KF) are identified using novel emulator-generated data by minimizing the KF residual so that identified models are accurate, consistent and reliable.The proposed tracking, fault-tolerant control, and design of the KF residuals-based design of soft sensor were successfully evaluated on a simulated laboratory-scale quadrotor.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".