1D34 NMPC Approach on Nonlinear Model Reduction Techniques : Application to an ultrasonic motor(The 12th International Conference on Motion and Vibration Control)
Bibliographic record
Abstract
This paper proposes a nonlinear control design methodology using nonlinear model order reduction (MOR) techniques on a practical complex system. As the typical controlled object, we take ultrasonic motor that has strong nonlinearity and needs precise control. In control design phase, Nonlinear Model Predictive Control (NMPC) is applied. In this paper, MOR of Finite Element method is described first. Generally, it is very difficult to apply MOR to a nonlinear finite element model. However, it may be possible by focusing to the character of the model. The key point of this MOR technique is separation with linear space and nonlinear space for system from the composition view point of ultrasonic motor. When the techniques apply to a certain system appropriately, the degree of freedom of the finite element model will be reduced substantially. This model will be called the reduction model, and it has tried to apply this technique to the transient response simulation of ultrasonic motor. A strong nonlinearity appears because ultrasonic motor is driven by the contact friction. We identify the nominal model for the control design from the reduction model. In order to get this nominal model, the conditions of an input and load are changed to a reduction model, and two or more simulations are performed. As a result, the gain constant and the time constant can be found. This gain constant and time constant turn into a nonlinear function whose variables are the input and load. The proposed method tries to carry out speed follow-up control with the application of model prediction control to the model. Finally, the control rule of a nominal model is applied also to reduction model, and the speed servo performance is checked.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".