A multi-switching mode intelligent hybrid control of electro-hydraulic proportional systems
Bibliographic record
Abstract
The electro-hydraulic proportional system is a highly non-linear system owing to the fact that the system is composed of components from different disciplines such as electric, hydraulic and mechanical disciplines. In general, a switching-based controller is suitable to the control of such a system. In this paper, a switching-based controller is proposed, which is called multi-switching mode intelligent hybrid control, for electro-hydraulic proportional systems. The novelty of the multi-switching mode intelligent hybrid control is that it integrates the PID control law, the neural-fuzzy control law, and the expert-based control law. To demonstrate the effectiveness of the proposed multi-switching mode intelligent hybrid control, both experiment and simulation were conducted. It is shown that the experimental result corresponds well with the simulation result. Further, the proposed control system was compared with the traditional ones such as PID and neural-fuzzy controller for a trajectory tracking task with an electro-hydraulic proportional, which shows that the proposed one is far superior to these traditional ones. Overall, there is evidence that the proposed multi-switching mode intelligent hybrid control is very effective. It is noted that though the idea of the switching-based control system to electro-hydraulic proportional systems may not be new, the specific integration of the member control laws along with specific control laws developed in this work to electro-hydraulic proportional systems is not reported in the literature and multi-switching mode intelligent hybrid control is potentially useful to other electro-hydraulic proportional systems.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".