Stabilization of an Inverted Robot Arm Using Neuro-Controller
Why this work is in the frame
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Bibliographic record
Abstract
Many systems exist in real control applications whose characteristics are difficult to be mathematically modeled, therefore performing the design of an adequate controller is a computationally complex task using the classical methods. Alternatively, neural networks prove to be a good tool in control systems design which can be used without the need to know the exact model. This paper aims at designing a neuro-controller that combines both supervised and adaptive neuro-control schemes. The supervised scheme mimics the classical PID controller off-line; while the adaptive scheme can adapt to the system uncertainty on-line, which could eliminate the need for an exact model. The objective of the proposed neural control system is to stabilize a robot arm and the resulting robot arm angles. However, an experimental set-up of an inverted pendulum rig mounted on a cart is used as the test-bed. The simulation results prove that the proposed adaptive neuro-control scheme successfully maintained the pendulum in an upright position at steady-state.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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