Nonsingular fast terminal sliding mode control for robotic manipulators: a synergistic approach integrating RBFNN and AESO
Bibliographic record
Abstract
This study proposes a composite control architecture combining radial basis function neural network (RBFNN) and adaptive gain extended state observer (AESO) to address trajectory tracking challenges in robotic manipulators under nonlinear dynamics, parametric uncertainties, and external disturbances. The framework integrates three core innovations: (1) a global nonsingular fast terminal sliding mode surface with finite-time convergence and singularity avoidance, which is augmented by an integral term for enhanced robustness and steady-state error suppression; (2) a dual compensation mechanism where RBFNN approximates unmodeled dynamics through nonlinear mapping, while AESO provides real-time estimation of lumped disturbances; (3) a stability guarantee derived through Lyapunov-based synthesis. Comparative simulations on a two-link manipulator demonstrate superior performance over conventional single compensation methods, with significant improvements in convergence speed, tracking precision, and torque smoothness. The results validate the efficacy of the framework in complex operational scenarios. However, although AESO can balance state estimation accuracy and noise immunity by dynamically adjusting bandwidth, noise amplification is inevitable when dealing with high-frequency noise due to inherent defects of gain adaptive mechanism.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".