PD-PD Type Learning Control for Uncertain Nonlinear Systems
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Bibliographic record
Abstract
In this paper, a new learning control, called PD-PD type learning control, is proposed for trajectory tracking of nonlinear systems with uncertainty and disturbance. In the developed control scheme, a PD feedback control with the current tracking errors and a PD type iterative learning control using the previous tracking errors are combined in the updating law. Explicit expressions have been developed for choosing the feedback control gains and the iterative learning gains, and an initial updating scheme is proposed to reduce and eliminate initial errors from iteration to iteration. It is proven that the final tracking error is guaranteed to converge toward the desired trajectory in the presence of varying uncertainty, disturbance, and initial errors. Comparing with the traditional iterative learning control, the new algorithm has potential benefits that include: fast convergence rate, more flexible choices of the learning gains, and monotonic convergence of the tracking error. The effectiveness of the proposed learning control method is demonstrated by simulation experiments. Due to the straightforward implementation and very good trajectory performance of the proposed control algorithm, it should be highly applicable to industrial systems.
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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