Discrete-Time H2-Optimal Control for Hydraulic Position Control Systems
Bibliographic record
Abstract
Hydraulic position control systems play an important role in industrial automation. This paper explores the application of discrete-time H2-optimal control for a hydraulic position control system (HPCS). By minimizing the H2-norm of the system, the discrete-time robust H2-optimal control both stabilizes the plant and minimizes the root-mean-square of the servo position error simultaneously. The intuitive nature of this advanced approach helps to manage the selection of design parameters, whereas, classical methods provide less insight into strategies for parameter selection and control design. Additionally, the powerful ability to address disturbances and uncertainty in the robust H2-optimal design offers a more direct alternative to the ad hoc and iterative nature of classical methods for the hydraulic servo position system. Computer simulations illustrate the design procedure and the effectiveness of the proposed method. Experimental studies which employ the H2-optimal control on a hydraulic positioning system are also conducted and the results show that the method is suitable for practical applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".