Stability Analysis of Input-Output Linearization Control With LuGre Friction Model Using Lyapunov Exponents
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Bibliographic record
Abstract
This work performs stability analysis on a control system partially modeled by LuGre friction model. Such a friction model has been proved sufficient to account for many features of friction. However, two parameters in the friction model, σ0 and σ1, are extremely difficult to be identified in real-world applications and the parameter errors (disturbances) coming from the measurements are therefore expected. In this work, we present a systematic methodology to rigorously analyze the effect of the disturbances mentioned above on the control system stability. The obtained analysis result clearly shows the robustness of the selected control law (input-output linearization control law). Lyapunov exponents calculated for two disturbed systems and our stability analysis result are in good agreement. The proposed methodology can be applied to other control laws of interest to examine their robustness.
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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 | 0.001 |
| 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 |
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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.
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