Novel finite-time adaptive sliding mode tracking control for disturbed mechanical systems
Bibliographic record
Abstract
In this paper, the finite-time tracking control problem of mechanical systems subject to model uncertainties and external disturbances is investigated. A new type of finite-time adaptive sliding mode control approach is proposed based on a novel integral sliding mode surface and a novel parametric adaptation mechanism. The integral sliding mode surface is originally designed by utilizing the adding a power integrator technique. The parametric adaptation mechanism is developed by using a single adaptive updating law to estimate the square of the upper bound of the lumped uncertain term. As compared with the most existing studies, the distinctive features of the proposed controller are threefold. (1) Benefiting from the novel integral sliding mode surface, the proposed controller has no singularity problem inherently existing in the terminal sliding mode control. (2) Owing to the use of novel parametric adaptation mechanism, the proposed controller is smooth and the unexpected chattering phenomenon is significantly attenuated. Moreover, the proposed controller is structurally simple and requires relatively few online calculations, which makes it affordable for practical applications. (3) The practical finite-time stability of the overall closed-loop system is strictly proved. The proposed controller can ensure the position and velocity tracking errors stabilize to the adjustable small neighborhoods around the origin in finite time. Lastly, the effectiveness and advantages of the proposed control approach are illustrated through simulations and comparisons.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".