A Continuous Velocity-Based Friction Model for Dynamics and Control With Physically Meaningful Parameters
Bibliographic record
Abstract
Friction is an important part of many dynamic systems, and, as a result, a good model of friction is necessary for simulating and controlling these systems. A new friction model, designed primarily for optimal control and real-time dynamic applications, is presented in this paper. This new model defines friction as a continuous function of velocity and captures the main velocity-dependent characteristics of friction: the Stribeck effect and viscous friction. Additional phenomena of friction such as microdisplacement and the time dependence of friction were not modeled due to the increased complexity of the model, leading to reduced performance of real-time simulations or optimizations. Unlike several current friction models, this model is C1 continuous and differentiable, which is desirable for optimal control applications, sensitivity analysis, and multibody dynamic analysis and simulation. To simplify parameter identification, the proposed model was designed to use a minimum number of parameters, all with physical meaning and readily visible on a force–velocity curve, rather than generic shape parameters. A simulation using the proposed model demonstrates that the model avoids any discontinuities in force at initial impact and the transition from slipping to sticking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".