Robust control for fuzzy electric power steering system: A two-layer performance approach
Bibliographic record
Abstract
This study investigates the angle tracking control of the electric power steering system, which is underactuated and with (possibly fast) time-varying uncertainties. We design the control based on constraint-following, that is, formulating the tracking goal as servo constraints. To tackle the uncertainty, especially the mismatched uncertainty, a robust control is proposed with two-layer performance: deterministically guaranteed and fuzzily optimized. Particularly, the control design is implemented in three steps. First, without considering uncertainty, a nominal control is designed. Second, an uncertainty decomposition technique is presented to account for uncertainty, which creatively allocates the mismatched uncertainty for the robust control design that also builds on the nominal system control. The robust scheme is deterministic without using any “if–then” rules and guarantees uniform boundedness and uniform ultimate boundedness for the system, that is, the deterministically guaranteed performance. Third, by using fuzzy set theory to describe uncertainty, a fuzzy-based performance index, including system performance and control cost, is introduced. A control parameter optimal design problem is formulated and analytically solved, that is, the fuzzily optimized performance. The effectiveness of the proposed approach is illustrated by rigorous proof and the simulation results on the electric power steering system.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".