Extended state observer–based improved non-singular fast terminal sliding mode for mobile manipulators
Bibliographic record
Abstract
The robust non-singular fast terminal sliding mode (NFTSM) controller is adopted in this work to solve the problem of tracking trajectory of a mobile manipulator (MM) suffering from uncertainties. The NFTSM method has the capability to ensure convergence rate and to provide good tracking accuracy and robustness against external perturbations and parameter uncertainties (total disturbances). However, the NFTSM controller needs high discontinuous gain to reject the effect of strong disturbances, which results in vibrations in the steady-state and chattering in the control law. To solve these issues, an extended state observer–based NFTSM technique is proposed for [Formula: see text]-degree of freedom (DoF) coupling MM. The proposed method is designed to approximate and compensate in real-time the uncertainties. It also ensures robustness against total disturbances, good convergence rate, and good tracking accuracy. The stability of the proposed control is also verified based on the Lyapunov theory. Experimental works are conducted on a 5-degree-of-freedom (5-DoF) MM where the results obtained demonstrate the effectiveness of the developed technique and prove the stability of the closed-loop system.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".