Backstepping-Based Nonsingular Terminal Sliding Mode Control for Finite-Time Trajectory Tracking of a Skid-Steer Mobile Robot
Bibliographic record
Abstract
Skid-steer mobile robots (SSMRs) are ubiquitous in indoor and outdoor applications. Their accurate trajectory tracking control is quite challenging due to the uncertainties arising from the complex behavior of frictional force, external disturbances, and fluctuations in the instantaneous center of rotation (ICR) during turning maneuvers. These uncertainties directly disturb velocities, hindering the robot from tracking the velocity command. This paper proposes a nonsingular terminal sliding mode control (NTSMC) based on backstepping for a four-wheel SSMR to cope with the aforementioned challenges. The strategy seeks to mitigate the impacts of external disturbances and model uncertainties by developing an adaptive law to estimate the integrated lumped outcome. The finite time stability of the closed-loop system is proven using Lyapunov’s theory. The designed NTSMC input is continuous and avoids noticeable chattering. It was noted in the simulation analysis that the proposed control strategy is strongly robust against disturbance and modeling uncertainties, demonstrating effective trajectory tracking performance in the presence of disturbance and modeling uncertainties.
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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) | 0.000 | 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".