Intelligent Learning Controllers for Nonlinear Systems using Radial Basis Neural Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Iterative learning controllers are a good choice for repetitive trajectory tracking tasks because they do not need identification of a nonlinear system. Starting from zero knowledge of the system, these types of learning controllers take a certain number of iterations before converging to the desired trajectory. In the case of many desired trajectories, learning takes almost same amount of iterations for every desired trajectory. In this article intelligence is incorporated in the iterative learning controllers using neural network for a class of nonlinear systems. The experience of iterative learning controller with different desired trajectories is stored in the neural network. For a new desired trajectory, this neural network generates the initial control input and feeds it to the iterative learning controller. This approach is proved to be very effective in improving the convergence of the tracking error. Our proposed method is very general and applicable to most of the iterative learning controllers without modifying their simple learning structures.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it