Sampled-data Control of a Class of Nonlinear Flat Systems With Application to Unicycle Trajectory Tracking
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Bibliographic record
Abstract
In this paper we propose a flatness-based nonlinear sampled-data control approach for the trajectory tracking of nonlinear differentially flat systems that can be expressed in cascade form. The nonlinear sampled-data control method relies on the flatness property for the generation of appropriate trajectories, with the design of one-step predictive control laws, and on controller discretization by means of an averaging-like method. In the paper we demonstrate that the causality problem that might arise in the implementation is avoided by using an estimator based on numerical integration techniques of sufficiently high order. Stability-like properties are proved. Numerical simulations show that the proposed sampled-data control law offers the best closed-loop performance when compared with nonlinear direct digital design for the trajectory tracking of a rotorcraft-like UAV modeled as the unicycle. The synthesis of the nonlinear sampled-data control law takes advantage of the feedback linearizability property of the unicycle model. Furthermore, the proposed nonlinear sampled-data control does not rely on approximated discretization techniques and is computed from exponentially convergent steering trajectories that result from the stabilization of the linearized unicycle model.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it