ADAPTIVE TRACKING CONTROL OF NONHOLONOMIC SYSTEMS BASED ON FEEDBACK ERROR LEARNING
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Bibliographic record
Abstract
In this paper, we present an adaptive feedback error learning (AFEL) control scheme that are suitable for a class of nonholonomic wheeled mobile robots with uncertainties. The proposed algorithm employs nonlinear function approximation with automatic growth of the neural network (NN) learning according to the nonlinearities and the working domain of the tracking control system. The unknown function in dynamical system is approximated by training nonlinear NN models, and, imperfect approximation errors of NNs are relaxed by designing parallel robust term. Lyapunov synthesis is proposed for AFEL control design with guaranteed stability. Inspired by composite adaptive control scheme, the proposed adaptive control algorithm employs both closed-loop tracking errors and estimation errors to optimize the parameters by NN online weight tuning algorithms, which guarantee small tracking errors and no loss of stability in robot motion with bounded input signals. We demonstrate superior tracking results using the proposed AFEL control method in various Matlab simulations.
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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 | 0.000 | 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 it