Point-to-Point Motion Trajectory Generation for Uncertain Systems: A Closed-Form Solution
Bibliographic record
Abstract
This paper investigates output feedback tracking control of Unmanned Aerial Vehicles using only the measured inertial coordinate. The output feedback control is based on two cascade high-gain observers combined with a full-state feedback control that is based on the backstepping approach. In this work, the backstepping controller is designed to solve the tracking control problem of the underactuated system. Then, an observer comprised of two cascaded high-gain observers with different speeds is considered; the faster observer provides estimates of the output position and velocity of the system in three dimensions and feeds a virtual nonlinear output to estimate the Euler angles (pitch, roll, and yaw) and angular velocity. We show that the equilibrium point of the full-state feedback control system under full knowledge of the system information is exponentially stable. The simulation results show that the output feedback control achieves the tracking control objective and recovers the performance of the state feedback control. Also, the simulation results show convergence and the boundedness of the estimation errors.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".