Sliding Mode Controller and Filter Applied to an Electrohydraulic Actuator System
Bibliographic record
Abstract
A common problem pertaining to linear or nonlinear systems is the design of a combined robust control and estimation strategy that can effectively deal with noise and uncertainties. The variable structure control (VSC) and its special form of sliding mode control (SMC) demonstrate robustness with regard to uncertainties, although their performance can be severely degraded by noise. As such they can benefit from using state estimates obtained from filters. In this regard, this paper considers the use of a recently proposed robust state and parameter estimation strategy referred to as the variable structure filter (VSF) in conjunction with SMC. The contribution of this paper is a new strategy that combines sliding mode control with the variable structure filter. In the presence of bounded parametric uncertainties and noise, this combined method demonstrates robust stability both in terms of control and state estimation. Furthermore, the combined strategy can be used to achieve high regulation rates or short settling time. The combined VSF and SMC strategy is demonstrated by its application to a high precision hydrostatic system, referred to as the electrohydraulic actuator 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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".