Output feedback adaptive controller of a autonomous skid-steering mobile vehicle based on sequential super-twisting differentiators
Bibliographic record
Abstract
The main purpose of this work is to develop an output state-dependent controller that solves the path-tracking deviation error for a skid-steering autonomous vehicle. The controller takes advantage of a nonlinear diffeomorphism that transforms skid-steering autonomous vehicle into a multi-input multi-output chain of integrators. This research assumes that available skid-steering autonomous vehicle variables are its position and its orientation. This in fact motivates the development of a modified super-twisting algorithm operating as a sequential step-by-step differentiator that estimates traslational velocity and acceleration of the studied autonomous vehicle in a finite time, which were used as part of the controller implementation. Based on the estimated states by the step-by-step multi-variable differentiator, an adaptive control design enforces the asymptotic convergence of the tracking trajectories for the skid-steering autonomous vehicle to the origin. The explicit form of the controller gains is derived using a class of control Lyapunov function including the deviation corresponding to the tracking error and a term that defines a matrix norm associated with control gains. Numerical results confirm the workability of the proposed controller considering the reduced norm of tracking error obtained with the proposed controller. Experimental evaluations compared the adaptive control introduced in this study and a state-feedback form justifying the control proposal. The adaptive form enforced smaller tracking errors using the estimated states forced by the step-by-step differentiator and the information obtained from a multi-camera video high-frequency acquisition system.
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.
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 | 0.000 |
| 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 | 0.001 | 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".