Comparison of stabilizing NMPC designs for wheeled mobile robots: An experimental study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, two stabilizing nonlinear model predictive control (NMPC) designs, namely, final-state equality constraint stabilizing design and final-state inequality constraint stabilizing design have been applied to achieve two wheeled mobile robot's control objectives, i.e. point stabilization and trajectory tracking. In both controllers, final-state constraints are imposed, on the online optimization step, to guarantee the closed loop stability. As shown in the literature, both stabilizing designs were addressed to be computationally intense; thus, their real-time implementation is not tractable. Nonetheless, in this work, a recently developed toolkit implementing fast NMPC routines has been used to apply the two stabilizing designs on a mobile robot research platform after developing a C++ code, coupling the toolkit and the research platform's software. Full scale experiments implementing the two stabilizing designs are conducted and contrasted in terms of performance measures and real-time requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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