Adaptive Sliding Mode Control with RBF Neural Network-Based Tuning Method for Parallel Robot
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Bibliographic record
Abstract
In this paper, a novel adaptive sliding mode control scheme with RBF (radial basis function) neural network-based tuning method is proposed for the trajectory tracking of a 6-RSS (Revolute-Spherical-Spherical) parallel robot in Cartesian space. Parallel robot is a highly nonlinear system with closed-chain mechanisms, which poses the major challenges to the controller design. The robust sliding mode controller is developed to deal with system uncertainties such as modeling errors, frictions, and disturbances. With strong adaptation and learning ability, RBF neural network is adopted to identify the parallel robot dynamics, and then the adaptive self-tuning of the control gains in the controller is realized, which is more flexible than manual tuning method and can guarantee the desired results of the changing system. The stability of the controller has been validated using Lyapunov theorem. Simulation results demonstrate that the proposed controller can achieve better tracking performance than the sliding mode controller with fixed control gains.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it