Deep-Recurrent-Neural-Network-Based Adaptive Sliding Mode Control for a 6-DOF Serial Robot
Bibliographic record
Abstract
The efficient control of serial robots in the presence of dynamic uncertainties and external disturbances is significant in many industrial applications. In this work, an adaptive sliding mode control (ASMC) approach with deep recurrent neural network (DRNN) is proposed for a 6-degree-of-freedom (6-DOF) industrial serial robot in joint space. A model-based sliding mode controller is developed to maintain the strong robustness of the robotic system. A deep recurrent neural network is designed to estimate the lumped system uncertainties in the controller. It consists of a feedforward structure through two hidden layers and a feedback loop from the output layer to the input layer, which exhibits more powerful online learning ability and dynamic property than shallow feedforward neural networks. According to Lyapunov theorem, the adaptation laws of the neural network parameters are derived, and the stability of the controller can be guaranteed. Simulation results demonstrate the effectiveness and superiority of the DRNN-based ASMC strategy regarding estimation convergence speed and trajectory tracking accuracy.
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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) | 0.001 | 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 | 0.001 | 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".