Disturbance Observer-Based Backstepping- Super Twisting Control for Robust Trajectory Tracking in Robot Manipulators
Bibliographic record
Abstract
This article presents a robust adaptive control design for robot manipulators to track desired trajectories amid unknown disturbances and input saturation. The suggested controller integrates backstepping and super-twisting techniques, ensuring system stability and robustness. The backstepping method mitigates unmatched disturbances in a two-step process, while the super-twisting algorithm addresses matched perturbations and overshoot apparitions. A nonlinear observer enhances control efficacy against matched disturbances and input saturation, ensuring fast convergence via a quasi-nonsingular terminal sliding surface. This approach enables precise tracking with smooth control signals and avoids large feedback gains. An advanced adaptive reaching law dynamically adjusts the controller's behavior through a potential function, mimicking and enhancing various established reaching control laws. The designed method provides a flexible strategy with rapid convergence, minimal chattering, and adaptability to variation of system dynamics. Stability is confirmed using Lyapunov’s direct method, proving uniform boundedness of signals in the closed-loop system. The proposed controller was validated through simulations, experiments, and comparative analysis, demonstrating its superior performance.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".