On-line optimization of RBF network feedforward compensation for load disturbance in idle speed control of automotive engine
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Bibliographic record
Abstract
The paper considers an application of a novel on-line nonlinear parametric optimization technique based on radial basis function (RBF) network approximation to the problem of idle speed control for an automotive engine. The control problem is formulated for a phenomenological model of an idling automotive engine. The system is highly nonlinear and includes delays in the control loop. In this paper, we assume that the load disturbance is known to the controller and demonstrate high-performance nonlinear adaptive feedforward compensation of this disturbance. The on-line parametric optimization technique applied in this paper to the design of the nonlinear adaptive feedforward controller uses an RBF network for approximating nonlinear vector fields defining the controller. The simulation results presented in this paper show that the designed feedforward controller provides good idle speed control performance and fast adaptation of the RBF network weights. The technique could be extended to other problems in powertrain control.
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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